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1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 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12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 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Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul 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data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header Overlay-header--divided"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="custom-scopes-dialog-title"> Saved searches </h1> <h2 id="custom-scopes-dialog-description" class="Overlay-description">Use saved searches to filter your results more quickly</h2> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="custom-scopes-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 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dir=\"auto\"\u003eDeep Reinforcement Learning in Keras\u003c/h1\u003e\u003ca id=\"user-content-deep-reinforcement-learning-in-keras\" class=\"anchor\" aria-label=\"Permalink: Deep Reinforcement Learning in Keras\" href=\"#deep-reinforcement-learning-in-keras\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eModular Implementation of popular Deep Reinforcement Learning algorithms in Keras:\u003c/p\u003e\n\u003cul class=\"contains-task-list\"\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\" checked=\"\"\u003e Synchronous N-step Advantage Actor Critic (\u003ca href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras#n-step-advantage-actor-critic-a2c\"\u003eA2C\u003c/a\u003e)\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\" checked=\"\"\u003e Asynchronous N-step Advantage Actor-Critic (\u003ca href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras#n-step-asynchronous-advantage-actor-critic-a3c\"\u003eA3C\u003c/a\u003e)\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\" checked=\"\"\u003e Deep Deterministic Policy Gradient with Parameter Noise (\u003ca href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras#deep-deterministic-policy-gradient-ddpg\"\u003eDDPG\u003c/a\u003e)\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\" checked=\"\"\u003e Double Deep Q-Network (\u003ca href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras#double-deep-q-network-ddqn\"\u003eDDQN\u003c/a\u003e)\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\" checked=\"\"\u003e Double Deep Q-Network with Prioritized Experience Replay (\u003ca href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras#\"\u003eDDQN + PER\u003c/a\u003e)\u003c/li\u003e\n\u003cli class=\"task-list-item\"\u003e\u003cinput type=\"checkbox\" id=\"\" disabled=\"\" class=\"task-list-item-checkbox\" checked=\"\"\u003e Dueling DDQN (\u003ca href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras#dueling-double-deep-q-network-dueling-ddqn\"\u003eD3QN\u003c/a\u003e)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eGetting Started\u003c/h2\u003e\u003ca id=\"user-content-getting-started\" class=\"anchor\" aria-label=\"Permalink: Getting Started\" href=\"#getting-started\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThis implementation requires keras 2.1.6, as well as OpenAI gym.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ pip install gym keras==2.1.6\"\u003e\u003cpre\u003e$ pip install gym keras==2.1.6\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eActor-Critic Algorithms\u003c/h1\u003e\u003ca id=\"user-content-actor-critic-algorithms\" class=\"anchor\" aria-label=\"Permalink: Actor-Critic Algorithms\" href=\"#actor-critic-algorithms\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eN-step Advantage Actor Critic (A2C)\u003c/h3\u003e\u003ca id=\"user-content-n-step-advantage-actor-critic-a2c\" class=\"anchor\" aria-label=\"Permalink: N-step Advantage Actor Critic (A2C)\" href=\"#n-step-advantage-actor-critic-a2c\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe Actor-Critic algorithm is a model-free, off-policy method where the critic acts as a value-function approximator, and the actor as a policy-function approximator. When training, the critic predicts the TD-Error and guides the learning of both itself and the actor. In practice, we approximate the TD-Error using the Advantage function. For more stability, we use a shared computational backbone across both networks, as well as an N-step formulation of the discounted rewards. We also incorporate an entropy regularization term (\"soft\" learning) to encourage exploration. While A2C is simple and efficient, running it on Atari Games quickly becomes intractable due to long computation time.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eN-step Asynchronous Advantage Actor Critic (A3C)\u003c/h3\u003e\u003ca id=\"user-content-n-step-asynchronous-advantage-actor-critic-a3c\" class=\"anchor\" aria-label=\"Permalink: N-step Asynchronous Advantage Actor Critic (A3C)\" href=\"#n-step-asynchronous-advantage-actor-critic-a3c\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn a similar fashion as the A2C algorithm, the implementation of A3C incorporates asynchronous weight updates, allowing for much faster computation. We use multiple agents to perform gradient ascent asynchronously, over multiple threads. We test A3C on the Atari Breakout environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/h3\u003e\u003ca id=\"user-content-deep-deterministic-policy-gradient-ddpg\" class=\"anchor\" aria-label=\"Permalink: Deep Deterministic Policy Gradient (DDPG)\" href=\"#deep-deterministic-policy-gradient-ddpg\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe DDPG algorithm is a model-free, off-policy algorithm for continuous action spaces. Similarly to A2C, it is an actor-critic algorithm in which the actor is trained on a deterministic target policy, and the critic predicts Q-Values. In order to reduce variance and increase stability, we use experience replay and separate target networks. Moreover, as hinted by \u003ca href=\"https://blog.openai.com/better-exploration-with-parameter-noise/\" rel=\"nofollow\"\u003eOpenAI\u003c/a\u003e, we encourage exploration through parameter space noise (as opposed to traditional action space noise). We test DDPG on the Lunar Lander environment.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ python3 main.py --type A2C --env CartPole-v1\n$ python3 main.py --type A3C --env CartPole-v1 --nb_episodes 10000 --n_threads 16\n$ python3 main.py --type A3C --env BreakoutNoFrameskip-v4 --is_atari --nb_episodes 10000 --n_threads 16\n$ python3 main.py --type DDPG --env LunarLanderContinuous-v2\"\u003e\u003cpre\u003e$ python3 main.py --type A2C --env CartPole-v1\n$ python3 main.py --type A3C --env CartPole-v1 --nb_episodes 10000 --n_threads 16\n$ python3 main.py --type A3C --env BreakoutNoFrameskip-v4 --is_atari --nb_episodes 10000 --n_threads 16\n$ python3 main.py --type DDPG --env LunarLanderContinuous-v2\u003c/pre\u003e\u003c/div\u003e\n\u003cbr\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras/blob/master/results/a2c.png?raw=true\"\u003e\u003cimg width=\"40%\" src=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras/raw/master/results/a2c.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras/blob/master/results/ddpg.png?raw=true\"\u003e\u003cimg width=\"40%\" src=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras/raw/master/results/ddpg.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/div\u003e \n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDeep Q-Learning Algorithms\u003c/h1\u003e\u003ca id=\"user-content-deep-q-learning-algorithms\" class=\"anchor\" aria-label=\"Permalink: Deep Q-Learning Algorithms\" href=\"#deep-q-learning-algorithms\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDouble Deep Q-Network (DDQN)\u003c/h3\u003e\u003ca id=\"user-content-double-deep-q-network-ddqn\" class=\"anchor\" aria-label=\"Permalink: Double Deep Q-Network (DDQN)\" href=\"#double-deep-q-network-ddqn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe DQN algorithm is a Q-learning algorithm, which uses a Deep Neural Network as a Q-value function approximator. We estimate target Q-values by leveraging the Bellman equation, and gather experience through an epsilon-greedy policy. For more stability, we sample past experiences randomly (Experience Replay). A variant of the DQN algorithm is the Double-DQN (or DDQN). For a more accurate estimation of our Q-values, we use a second network to temper the overestimations of the Q-values by the original network. This \u003cem\u003etarget\u003c/em\u003e network is updated at a slower rate Tau, at every training step.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDouble Deep Q-Network with Prioritized Experience Replay (DDQN + PER)\u003c/h3\u003e\u003ca id=\"user-content-double-deep-q-network-with-prioritized-experience-replay-ddqn--per\" class=\"anchor\" aria-label=\"Permalink: Double Deep Q-Network with Prioritized Experience Replay (DDQN + PER)\" href=\"#double-deep-q-network-with-prioritized-experience-replay-ddqn--per\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe can further improve our DDQN algorithm by adding in Prioritized Experience Replay (PER), which aims at performing importance sampling on the gathered experience. The experience is ranked by its TD-Error, and stored in a SumTree structure, which allows efficient retrieval of the \u003cem\u003e(s, a, r, s')\u003c/em\u003e transitions with the highest error.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDueling Double Deep Q-Network (Dueling DDQN)\u003c/h3\u003e\u003ca id=\"user-content-dueling-double-deep-q-network-dueling-ddqn\" class=\"anchor\" aria-label=\"Permalink: Dueling Double Deep Q-Network (Dueling DDQN)\" href=\"#dueling-double-deep-q-network-dueling-ddqn\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIn the dueling variant of the DQN, we incorporate an intermediate layer in the Q-Network to estimate both the state value and the state-dependent advantage function. After reformulation (see \u003ca href=\"https://arxiv.org/pdf/1511.06581.pdf\" rel=\"nofollow\"\u003eref\u003c/a\u003e), it turns out we can express the estimated Q-Value as the state value, to which we add the advantage estimate and subtract its mean. This factorization of state-independent and state-dependent values helps disentangling learning across actions and yields better results.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eRunning\u003c/h3\u003e\u003ca id=\"user-content-running-1\" class=\"anchor\" aria-label=\"Permalink: Running\" href=\"#running-1\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64\n$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --with_PER\n$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --dueling\"\u003e\u003cpre\u003e$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64\n$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --with_PER\n$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --dueling\u003c/pre\u003e\u003c/div\u003e\n\u003cbr\u003e\n\u003cdiv align=\"center\" dir=\"auto\"\u003e\n\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras/blob/master/results/ddqn.png?raw=true\"\u003e\u003cimg width=\"60%\" src=\"https://github.com/germain-hug/Advanced-Deep-RL-Keras/raw/master/results/ddqn.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/div\u003e \n\u003cbr\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eArguments\u003c/h3\u003e\u003ca id=\"user-content-arguments\" class=\"anchor\" aria-label=\"Permalink: Arguments\" href=\"#arguments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cmarkdown-accessiblity-table\u003e\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003eArgument \u003c/th\u003e\n\u003cth align=\"left\"\u003eDescription\u003c/th\u003e\n\u003cth align=\"left\"\u003eValues\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--type\u003c/td\u003e\n\u003ctd align=\"left\"\u003eType of RL Algorithm to run\u003c/td\u003e\n\u003ctd align=\"left\"\u003eChoose from {A2C, A3C, DDQN, DDPG}\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--env\u003c/td\u003e\n\u003ctd align=\"left\"\u003eSpecify the environment\u003c/td\u003e\n\u003ctd align=\"left\"\u003eBreakoutNoFrameskip-v4 (default)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--nb_episodes\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNumber of episodes to run\u003c/td\u003e\n\u003ctd align=\"left\"\u003e5000 (default)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--batch_size\u003c/td\u003e\n\u003ctd align=\"left\"\u003eBatch Size (DDQN, DDPG)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e32 (default)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--consecutive_frames\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNumber of stacked consecutive frames\u003c/td\u003e\n\u003ctd align=\"left\"\u003e4 (default)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--is_atari\u003c/td\u003e\n\u003ctd align=\"left\"\u003eWhether the environment is an Atari Game with pixel input\u003c/td\u003e\n\u003ctd align=\"left\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--with_PER\u003c/td\u003e\n\u003ctd align=\"left\"\u003eWhether to use Prioritized Experience Replay (with DDQN)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--dueling\u003c/td\u003e\n\u003ctd align=\"left\"\u003eWhether to use Dueling Networks (with DDQN)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--n_threads\u003c/td\u003e\n\u003ctd align=\"left\"\u003eNumber of threads (A3C)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e16 (default)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--gather_stats\u003c/td\u003e\n\u003ctd align=\"left\"\u003eWhether to compute stats of scores averaged over 10 games (slow, see below)\u003c/td\u003e\n\u003ctd align=\"left\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--render\u003c/td\u003e\n\u003ctd align=\"left\"\u003eWhether to render the environment as it is training\u003c/td\u003e\n\u003ctd align=\"left\"\u003e-\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e--gpu\u003c/td\u003e\n\u003ctd align=\"left\"\u003eGPU index\u003c/td\u003e\n\u003ctd align=\"left\"\u003e0\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\u003c/markdown-accessiblity-table\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eVisualization \u0026amp; Monitoring\u003c/h1\u003e\u003ca id=\"user-content-visualization--monitoring\" class=\"anchor\" aria-label=\"Permalink: Visualization \u0026amp; Monitoring\" href=\"#visualization--monitoring\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eModel Visualization\u003c/h3\u003e\u003ca id=\"user-content-model-visualization\" class=\"anchor\" aria-label=\"Permalink: Model Visualization\" href=\"#model-visualization\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAll models are saved under \u003ccode\u003e\u0026lt;algorithm_folder\u0026gt;/models/\u003c/code\u003e when finished training. You can visualize them running in the same environment they were trained in by running the \u003ccode\u003eload_and_run.py\u003c/code\u003e script. For DQN models, you should specify the path to the desired model in the \u003ccode\u003e--model_path\u003c/code\u003e argument. For actor-critic models, you need to specify both weight files in the \u003ccode\u003e--actor_path\u003c/code\u003e and \u003ccode\u003e--critic_path\u003c/code\u003e arguments.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTensorboard monitoring\u003c/h3\u003e\u003ca id=\"user-content-tensorboard-monitoring\" class=\"anchor\" aria-label=\"Permalink: Tensorboard monitoring\" href=\"#tensorboard-monitoring\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eUsing tensorboard, you can monitor the agent's score as it is training. When training, a log folder with the name matching the chosen environment will be created. For example, to follow the A2C progression on CartPole-v1, simply run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"$ tensorboard --logdir=A2C/tensorboard_CartPole-v1/\"\u003e\u003cpre\u003e$ tensorboard --logdir=A2C/tensorboard_CartPole-v1/\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eResults plotting\u003c/h3\u003e\u003ca id=\"user-content-results-plotting\" class=\"anchor\" aria-label=\"Permalink: Results plotting\" href=\"#results-plotting\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWhen training with the argument\u003ccode\u003e--gather_stats\u003c/code\u003e, a log file is generated containing scores averaged over 10 games at every episode: \u003ccode\u003elogs.csv\u003c/code\u003e. Using \u003ca href=\"https://plot.ly/\" rel=\"nofollow\"\u003eplotly\u003c/a\u003e, you can visualize the average reward per episode.\nTo do so, you will first need to install plotly and get a \u003ca href=\"https://plot.ly/python/getting-started/\" rel=\"nofollow\"\u003efree licence\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip3 install plotly\"\u003e\u003cpre\u003epip3 install plotly\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo set up your credentials, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import plotly\nplotly.tools.set_credentials_file(username='\u0026lt;your_username\u0026gt;', api_key='\u0026lt;your_key\u0026gt;')\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eplotly\u003c/span\u003e\n\u003cspan class=\"pl-s1\"\u003eplotly\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003etools\u003c/span\u003e.\u003cspan class=\"pl-c1\"\u003eset_credentials_file\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eusername\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'\u0026lt;your_username\u0026gt;'\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eapi_key\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e'\u0026lt;your_key\u0026gt;'\u003c/span\u003e)\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFinally, to plot the results, run:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"python3 utils/plot_results.py \u0026lt;path_to_your_log_file\u0026gt;\"\u003e\u003cpre\u003epython3 utils/plot_results.py \u003cspan class=\"pl-k\"\u003e\u0026lt;\u003c/span\u003epath_to_your_log_file\u003cspan class=\"pl-k\"\u003e\u0026gt;\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAcknowledgments\u003c/h1\u003e\u003ca id=\"user-content-acknowledgments\" class=\"anchor\" aria-label=\"Permalink: Acknowledgments\" href=\"#acknowledgments\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAtari Environment Helper Class template by \u003ca href=\"https://github.com/ShanHaoYu/Deep-Q-Network-Breakout/blob/master/environment.py\"\u003e@ShanHaoYu\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eAtari Environment Wrappers by \u003ca href=\"/germain-hug/Deep-RL-Keras/blob/master/github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py\"\u003eOpenAI\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eSumTree Helper Class by \u003ca href=\"https://github.com/jaara/AI-blog/blob/master/SumTree.py\"\u003e@jaara\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReferences (Papers)\u003c/h1\u003e\u003ca id=\"user-content-references-papers\" class=\"anchor\" aria-label=\"Permalink: References (Papers)\" href=\"#references-papers\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf\" rel=\"nofollow\"\u003eAdvantage Actor Critic (A2C)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1602.01783.pdf\" rel=\"nofollow\"\u003eAsynchronous Advantage Actor Critic (A3C)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"http://proceedings.mlr.press/v32/silver14.pdf\" rel=\"nofollow\"\u003eDeep Deterministic Policy Gradient (DDPG)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1707.01495.pdf\" rel=\"nofollow\"\u003eHindsight Experience Replay (HER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf\" rel=\"nofollow\"\u003eDeep Q-Learning (DQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1509.06461.pdf\" rel=\"nofollow\"\u003eDouble Q-Learning (DDQN)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1511.05952.pdf\" rel=\"nofollow\"\u003ePrioritized Experience Replay (PER)\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://arxiv.org/pdf/1511.06581.pdf\" rel=\"nofollow\"\u003eDueling Network Architectures (D3QN)\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Deep Reinforcement Learning in Keras","anchor":"deep-reinforcement-learning-in-keras","htmlText":"Deep Reinforcement Learning in Keras"},{"level":2,"text":"Getting Started","anchor":"getting-started","htmlText":"Getting Started"},{"level":1,"text":"Actor-Critic Algorithms","anchor":"actor-critic-algorithms","htmlText":"Actor-Critic Algorithms"},{"level":3,"text":"N-step Advantage Actor Critic (A2C)","anchor":"n-step-advantage-actor-critic-a2c","htmlText":"N-step Advantage Actor Critic (A2C)"},{"level":3,"text":"N-step Asynchronous Advantage Actor Critic (A3C)","anchor":"n-step-asynchronous-advantage-actor-critic-a3c","htmlText":"N-step Asynchronous Advantage Actor Critic (A3C)"},{"level":3,"text":"Deep Deterministic Policy Gradient (DDPG)","anchor":"deep-deterministic-policy-gradient-ddpg","htmlText":"Deep Deterministic Policy Gradient (DDPG)"},{"level":3,"text":"Running","anchor":"running","htmlText":"Running"},{"level":1,"text":"Deep Q-Learning Algorithms","anchor":"deep-q-learning-algorithms","htmlText":"Deep Q-Learning Algorithms"},{"level":3,"text":"Double Deep Q-Network (DDQN)","anchor":"double-deep-q-network-ddqn","htmlText":"Double Deep Q-Network (DDQN)"},{"level":3,"text":"Double Deep Q-Network with Prioritized Experience Replay (DDQN + PER)","anchor":"double-deep-q-network-with-prioritized-experience-replay-ddqn--per","htmlText":"Double Deep Q-Network with Prioritized Experience Replay (DDQN + PER)"},{"level":3,"text":"Dueling Double Deep Q-Network (Dueling DDQN)","anchor":"dueling-double-deep-q-network-dueling-ddqn","htmlText":"Dueling Double Deep Q-Network (Dueling DDQN)"},{"level":3,"text":"Running","anchor":"running-1","htmlText":"Running"},{"level":3,"text":"Arguments","anchor":"arguments","htmlText":"Arguments"},{"level":1,"text":"Visualization \u0026 Monitoring","anchor":"visualization--monitoring","htmlText":"Visualization \u0026amp; Monitoring"},{"level":3,"text":"Model Visualization","anchor":"model-visualization","htmlText":"Model Visualization"},{"level":3,"text":"Tensorboard monitoring","anchor":"tensorboard-monitoring","htmlText":"Tensorboard monitoring"},{"level":3,"text":"Results plotting","anchor":"results-plotting","htmlText":"Results plotting"},{"level":1,"text":"Acknowledgments","anchor":"acknowledgments","htmlText":"Acknowledgments"},{"level":1,"text":"References (Papers)","anchor":"references-papers","htmlText":"References 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data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Deep Reinforcement Learning in Keras</h1><a id="user-content-deep-reinforcement-learning-in-keras" class="anchor" aria-label="Permalink: Deep Reinforcement Learning in Keras" href="#deep-reinforcement-learning-in-keras"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Modular Implementation of popular Deep Reinforcement Learning algorithms in Keras:</p> <ul class="contains-task-list"> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox" checked=""> Synchronous N-step Advantage Actor Critic (<a href="https://github.com/germain-hug/Advanced-Deep-RL-Keras#n-step-advantage-actor-critic-a2c">A2C</a>)</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox" checked=""> Asynchronous N-step Advantage Actor-Critic (<a href="https://github.com/germain-hug/Advanced-Deep-RL-Keras#n-step-asynchronous-advantage-actor-critic-a3c">A3C</a>)</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox" checked=""> Deep Deterministic Policy Gradient with Parameter Noise (<a href="https://github.com/germain-hug/Advanced-Deep-RL-Keras#deep-deterministic-policy-gradient-ddpg">DDPG</a>)</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox" checked=""> Double Deep Q-Network (<a href="https://github.com/germain-hug/Advanced-Deep-RL-Keras#double-deep-q-network-ddqn">DDQN</a>)</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox" checked=""> Double Deep Q-Network with Prioritized Experience Replay (<a href="https://github.com/germain-hug/Advanced-Deep-RL-Keras#">DDQN + PER</a>)</li> <li class="task-list-item"><input type="checkbox" id="" disabled="" class="task-list-item-checkbox" checked=""> Dueling DDQN (<a href="https://github.com/germain-hug/Advanced-Deep-RL-Keras#dueling-double-deep-q-network-dueling-ddqn">D3QN</a>)</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Getting Started</h2><a id="user-content-getting-started" class="anchor" aria-label="Permalink: Getting Started" href="#getting-started"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">This implementation requires keras 2.1.6, as well as OpenAI gym.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ pip install gym keras==2.1.6"><pre>$ pip install gym keras==2.1.6</pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Actor-Critic Algorithms</h1><a id="user-content-actor-critic-algorithms" class="anchor" aria-label="Permalink: Actor-Critic Algorithms" href="#actor-critic-algorithms"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">N-step Advantage Actor Critic (A2C)</h3><a id="user-content-n-step-advantage-actor-critic-a2c" class="anchor" aria-label="Permalink: N-step Advantage Actor Critic (A2C)" href="#n-step-advantage-actor-critic-a2c"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The Actor-Critic algorithm is a model-free, off-policy method where the critic acts as a value-function approximator, and the actor as a policy-function approximator. When training, the critic predicts the TD-Error and guides the learning of both itself and the actor. In practice, we approximate the TD-Error using the Advantage function. For more stability, we use a shared computational backbone across both networks, as well as an N-step formulation of the discounted rewards. We also incorporate an entropy regularization term ("soft" learning) to encourage exploration. While A2C is simple and efficient, running it on Atari Games quickly becomes intractable due to long computation time.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">N-step Asynchronous Advantage Actor Critic (A3C)</h3><a id="user-content-n-step-asynchronous-advantage-actor-critic-a3c" class="anchor" aria-label="Permalink: N-step Asynchronous Advantage Actor Critic (A3C)" href="#n-step-asynchronous-advantage-actor-critic-a3c"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">In a similar fashion as the A2C algorithm, the implementation of A3C incorporates asynchronous weight updates, allowing for much faster computation. We use multiple agents to perform gradient ascent asynchronously, over multiple threads. We test A3C on the Atari Breakout environment.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Deep Deterministic Policy Gradient (DDPG)</h3><a id="user-content-deep-deterministic-policy-gradient-ddpg" class="anchor" aria-label="Permalink: Deep Deterministic Policy Gradient (DDPG)" href="#deep-deterministic-policy-gradient-ddpg"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The DDPG algorithm is a model-free, off-policy algorithm for continuous action spaces. Similarly to A2C, it is an actor-critic algorithm in which the actor is trained on a deterministic target policy, and the critic predicts Q-Values. In order to reduce variance and increase stability, we use experience replay and separate target networks. Moreover, as hinted by <a href="https://blog.openai.com/better-exploration-with-parameter-noise/" rel="nofollow">OpenAI</a>, we encourage exploration through parameter space noise (as opposed to traditional action space noise). We test DDPG on the Lunar Lander environment.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Running</h3><a id="user-content-running" class="anchor" aria-label="Permalink: Running" href="#running"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ python3 main.py --type A2C --env CartPole-v1 $ python3 main.py --type A3C --env CartPole-v1 --nb_episodes 10000 --n_threads 16 $ python3 main.py --type A3C --env BreakoutNoFrameskip-v4 --is_atari --nb_episodes 10000 --n_threads 16 $ python3 main.py --type DDPG --env LunarLanderContinuous-v2"><pre>$ python3 main.py --type A2C --env CartPole-v1 $ python3 main.py --type A3C --env CartPole-v1 --nb_episodes 10000 --n_threads 16 $ python3 main.py --type A3C --env BreakoutNoFrameskip-v4 --is_atari --nb_episodes 10000 --n_threads 16 $ python3 main.py --type DDPG --env LunarLanderContinuous-v2</pre></div> <br> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/germain-hug/Advanced-Deep-RL-Keras/blob/master/results/a2c.png?raw=true"><img width="40%" src="https://github.com/germain-hug/Advanced-Deep-RL-Keras/raw/master/results/a2c.png?raw=true" style="max-width: 100%;"></a> <a target="_blank" rel="noopener noreferrer" href="https://github.com/germain-hug/Advanced-Deep-RL-Keras/blob/master/results/ddpg.png?raw=true"><img width="40%" src="https://github.com/germain-hug/Advanced-Deep-RL-Keras/raw/master/results/ddpg.png?raw=true" style="max-width: 100%;"></a></div> <br> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Deep Q-Learning Algorithms</h1><a id="user-content-deep-q-learning-algorithms" class="anchor" aria-label="Permalink: Deep Q-Learning Algorithms" href="#deep-q-learning-algorithms"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Double Deep Q-Network (DDQN)</h3><a id="user-content-double-deep-q-network-ddqn" class="anchor" aria-label="Permalink: Double Deep Q-Network (DDQN)" href="#double-deep-q-network-ddqn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The DQN algorithm is a Q-learning algorithm, which uses a Deep Neural Network as a Q-value function approximator. We estimate target Q-values by leveraging the Bellman equation, and gather experience through an epsilon-greedy policy. For more stability, we sample past experiences randomly (Experience Replay). A variant of the DQN algorithm is the Double-DQN (or DDQN). For a more accurate estimation of our Q-values, we use a second network to temper the overestimations of the Q-values by the original network. This <em>target</em> network is updated at a slower rate Tau, at every training step.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Double Deep Q-Network with Prioritized Experience Replay (DDQN + PER)</h3><a id="user-content-double-deep-q-network-with-prioritized-experience-replay-ddqn--per" class="anchor" aria-label="Permalink: Double Deep Q-Network with Prioritized Experience Replay (DDQN + PER)" href="#double-deep-q-network-with-prioritized-experience-replay-ddqn--per"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We can further improve our DDQN algorithm by adding in Prioritized Experience Replay (PER), which aims at performing importance sampling on the gathered experience. The experience is ranked by its TD-Error, and stored in a SumTree structure, which allows efficient retrieval of the <em>(s, a, r, s')</em> transitions with the highest error.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Dueling Double Deep Q-Network (Dueling DDQN)</h3><a id="user-content-dueling-double-deep-q-network-dueling-ddqn" class="anchor" aria-label="Permalink: Dueling Double Deep Q-Network (Dueling DDQN)" href="#dueling-double-deep-q-network-dueling-ddqn"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">In the dueling variant of the DQN, we incorporate an intermediate layer in the Q-Network to estimate both the state value and the state-dependent advantage function. After reformulation (see <a href="https://arxiv.org/pdf/1511.06581.pdf" rel="nofollow">ref</a>), it turns out we can express the estimated Q-Value as the state value, to which we add the advantage estimate and subtract its mean. This factorization of state-independent and state-dependent values helps disentangling learning across actions and yields better results.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Running</h3><a id="user-content-running-1" class="anchor" aria-label="Permalink: Running" href="#running-1"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 $ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --with_PER $ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --dueling"><pre>$ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 $ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --with_PER $ python3 main.py --type DDQN --env CartPole-v1 --batch_size 64 --dueling</pre></div> <br> <div align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/germain-hug/Advanced-Deep-RL-Keras/blob/master/results/ddqn.png?raw=true"><img width="60%" src="https://github.com/germain-hug/Advanced-Deep-RL-Keras/raw/master/results/ddqn.png?raw=true" style="max-width: 100%;"></a></div> <br> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Arguments</h3><a id="user-content-arguments" class="anchor" aria-label="Permalink: Arguments" href="#arguments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <markdown-accessiblity-table><table> <thead> <tr> <th align="left">Argument </th> <th align="left">Description</th> <th align="left">Values</th> </tr> </thead> <tbody> <tr> <td align="left">--type</td> <td align="left">Type of RL Algorithm to run</td> <td align="left">Choose from {A2C, A3C, DDQN, DDPG}</td> </tr> <tr> <td align="left">--env</td> <td align="left">Specify the environment</td> <td align="left">BreakoutNoFrameskip-v4 (default)</td> </tr> <tr> <td align="left">--nb_episodes</td> <td align="left">Number of episodes to run</td> <td align="left">5000 (default)</td> </tr> <tr> <td align="left">--batch_size</td> <td align="left">Batch Size (DDQN, DDPG)</td> <td align="left">32 (default)</td> </tr> <tr> <td align="left">--consecutive_frames</td> <td align="left">Number of stacked consecutive frames</td> <td align="left">4 (default)</td> </tr> <tr> <td align="left">--is_atari</td> <td align="left">Whether the environment is an Atari Game with pixel input</td> <td align="left">-</td> </tr> <tr> <td align="left">--with_PER</td> <td align="left">Whether to use Prioritized Experience Replay (with DDQN)</td> <td align="left">-</td> </tr> <tr> <td align="left">--dueling</td> <td align="left">Whether to use Dueling Networks (with DDQN)</td> <td align="left">-</td> </tr> <tr> <td align="left">--n_threads</td> <td align="left">Number of threads (A3C)</td> <td align="left">16 (default)</td> </tr> <tr> <td align="left">--gather_stats</td> <td align="left">Whether to compute stats of scores averaged over 10 games (slow, see below)</td> <td align="left">-</td> </tr> <tr> <td align="left">--render</td> <td align="left">Whether to render the environment as it is training</td> <td align="left">-</td> </tr> <tr> <td align="left">--gpu</td> <td align="left">GPU index</td> <td align="left">0</td> </tr> </tbody> </table></markdown-accessiblity-table> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Visualization & Monitoring</h1><a id="user-content-visualization--monitoring" class="anchor" aria-label="Permalink: Visualization & Monitoring" href="#visualization--monitoring"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Model Visualization</h3><a id="user-content-model-visualization" class="anchor" aria-label="Permalink: Model Visualization" href="#model-visualization"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">All models are saved under <code><algorithm_folder>/models/</code> when finished training. You can visualize them running in the same environment they were trained in by running the <code>load_and_run.py</code> script. For DQN models, you should specify the path to the desired model in the <code>--model_path</code> argument. For actor-critic models, you need to specify both weight files in the <code>--actor_path</code> and <code>--critic_path</code> arguments.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Tensorboard monitoring</h3><a id="user-content-tensorboard-monitoring" class="anchor" aria-label="Permalink: Tensorboard monitoring" href="#tensorboard-monitoring"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Using tensorboard, you can monitor the agent's score as it is training. When training, a log folder with the name matching the chosen environment will be created. For example, to follow the A2C progression on CartPole-v1, simply run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="$ tensorboard --logdir=A2C/tensorboard_CartPole-v1/"><pre>$ tensorboard --logdir=A2C/tensorboard_CartPole-v1/</pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Results plotting</h3><a id="user-content-results-plotting" class="anchor" aria-label="Permalink: Results plotting" href="#results-plotting"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">When training with the argument<code>--gather_stats</code>, a log file is generated containing scores averaged over 10 games at every episode: <code>logs.csv</code>. Using <a href="https://plot.ly/" rel="nofollow">plotly</a>, you can visualize the average reward per episode. To do so, you will first need to install plotly and get a <a href="https://plot.ly/python/getting-started/" rel="nofollow">free licence</a>.</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip3 install plotly"><pre>pip3 install plotly</pre></div> <p dir="auto">To set up your credentials, run:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import plotly plotly.tools.set_credentials_file(username='<your_username>', api_key='<your_key>')"><pre><span class="pl-k">import</span> <span class="pl-s1">plotly</span> <span class="pl-s1">plotly</span>.<span class="pl-c1">tools</span>.<span class="pl-c1">set_credentials_file</span>(<span class="pl-s1">username</span><span class="pl-c1">=</span><span class="pl-s">'<your_username>'</span>, <span class="pl-s1">api_key</span><span class="pl-c1">=</span><span class="pl-s">'<your_key>'</span>)</pre></div> <p dir="auto">Finally, to plot the results, run:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="python3 utils/plot_results.py <path_to_your_log_file>"><pre>python3 utils/plot_results.py <span class="pl-k"><</span>path_to_your_log_file<span class="pl-k">></span></pre></div> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Acknowledgments</h1><a id="user-content-acknowledgments" class="anchor" aria-label="Permalink: Acknowledgments" href="#acknowledgments"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>Atari Environment Helper Class template by <a href="https://github.com/ShanHaoYu/Deep-Q-Network-Breakout/blob/master/environment.py">@ShanHaoYu</a></li> <li>Atari Environment Wrappers by <a href="/germain-hug/Deep-RL-Keras/blob/master/github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py">OpenAI</a></li> <li>SumTree Helper Class by <a href="https://github.com/jaara/AI-blog/blob/master/SumTree.py">@jaara</a></li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">References (Papers)</h1><a id="user-content-references-papers" class="anchor" aria-label="Permalink: References (Papers)" href="#references-papers"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf" rel="nofollow">Advantage Actor Critic (A2C)</a></li> <li><a href="https://arxiv.org/pdf/1602.01783.pdf" rel="nofollow">Asynchronous Advantage Actor Critic (A3C)</a></li> <li><a href="http://proceedings.mlr.press/v32/silver14.pdf" rel="nofollow">Deep Deterministic Policy Gradient (DDPG)</a></li> <li><a href="https://arxiv.org/pdf/1707.01495.pdf" rel="nofollow">Hindsight Experience Replay (HER)</a></li> <li><a href="https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf" rel="nofollow">Deep Q-Learning (DQN)</a></li> <li><a href="https://arxiv.org/pdf/1509.06461.pdf" rel="nofollow">Double Q-Learning (DDQN)</a></li> <li><a href="https://arxiv.org/pdf/1511.05952.pdf" rel="nofollow">Prioritized Experience Replay (PER)</a></li> <li><a href="https://arxiv.org/pdf/1511.06581.pdf" rel="nofollow">Dueling Network Architectures (D3QN)</a></li> </ul> </article></div></div></div></div></div>   <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="l3Kt94D3xw3Occ0GKaM/mB9M5u+025kAiMeGP+2OXPW9mEN6uGYlEDPzUsf60pcOyN8bYI8/s4rRTOgxa8QhxQ==" /> </div> <div 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topic-tag-link"> gym </a> <a href="/topics/policy-gradient" title="Topic: policy-gradient" data-view-component="true" class="topic-tag topic-tag-link"> policy-gradient </a> <a href="/topics/a3c" title="Topic: a3c" data-view-component="true" class="topic-tag topic-tag-link"> a3c </a> <a href="/topics/ddpg" title="Topic: ddpg" data-view-component="true" class="topic-tag topic-tag-link"> ddpg </a> <a href="/topics/ddqn" title="Topic: ddqn" data-view-component="true" class="topic-tag topic-tag-link"> ddqn </a> <a href="/topics/keras-rl" title="Topic: keras-rl" data-view-component="true" class="topic-tag topic-tag-link"> keras-rl </a> <a href="/topics/a2c" title="Topic: a2c" data-view-component="true" class="topic-tag topic-tag-link"> a2c </a> <a href="/topics/d3qn" title="Topic: d3qn" data-view-component="true" class="topic-tag topic-tag-link"> d3qn </a> <a href="/topics/dueling" title="Topic: dueling" data-view-component="true" class="topic-tag topic-tag-link"> dueling </a> </div> </div> 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