SARSA (State-Action-Reward-State-Action) in Reinforcement Learning

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target="_self">Beginner's DSA Sheet</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=50746f92a895c22a50504ac0c1fb9c84&sortBy=submissions&sprint_name=Top 50 Array Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 Array Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=57184072610b884e5df3584cc534115d&sortBy=submissions&sprint_name=Top 50 String Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 String Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=93d672753b74440c7427214c8ebf866d&sortBy=submissions&sprint_name=Top 50 DP Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 DP Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=405e9db0f353691ad3b2d546b19145e9&sortBy=submissions&sprint_name=Top 50 Graph Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 Graph Problems</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/explore?page=1&sprint=5f24de84b65bf7c4f4399c8111e26b81&sortBy=submissions&sprint_name=Top 50 Tree Problems&itm_source=geeksforgeeks&itm_medium=main_header_outIndia&itm_campaign=DSA_Header" target="_self">Top 50 Tree Problems</a></li></ul></li></ul></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/competitive-programming-a-complete-guide/?ref=outind" target="_self">Competitive Programming</a></li><li class="mega-dropdown__list-item" data-parent="false" aria-expanded="true" data-expandable="true"><span>Company Wise SDE Sheets</span><i class="gfg-icon gfg-icon_arrow-right"></i><ul class="mega-dropdown Screen_2"><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/facebookmeta-sde-sheet-interview-questions-and-answers/?ref=outind" target="_self">Facebook SDE Sheet</a></li><li class="mega-dropdown__list-item" data-child="true" aria-expanded="false" data-expandable="false"><a href="https://www.geeksforgeeks.org/amazon-sde-sheet-interview-questions-and-answers/?ref=outind" target="_self">Amazon SDE Sheet</a></li><li 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class="three_dot_dropdown_div three_dot_dropdown_reportarticle"> <div class="three_dot_dropdown_inner_div" data-gfg-action="report-article" onclick="report_article();"> <i class="gfg-icon gfg-icon_report"></i> <span>Report</span> </div> </div>  </div> <div class="article--viewer_Gnews tooltip"> <a class="three_dot_dropdown_inner_div Gnews_wrapper" href ='https://news.google.com/publications/CAAqBwgKMLTrzwsw44bnAw?hl=en-IN&gl=IN&ceid=IN%3Aen' target="_blank"> <img class="gfg-icon_Gnews no-zoom-in-cursor" src=https://media.geeksforgeeks.org/auth-dashboard-uploads/Google-news.svg loading="lazy" alt="News"></img> <span class='kebab_menu_news_text'>Follow</span> </a> </div> </div> </div> <div class="text"> <p dir="ltr"><b><strong>SARSA (State-Action-Reward-State-Action)</strong></b><span> is an on-policy learning algorithm used for this purpose. It helps an agent learn an optimal policy based on experience, where the agent improves its policy while continuously interacting with the environment.</span></p> <p dir="ltr"><span>SARSA outlines the key components of the RL process:</span></p><div id="GFG_AD_gfg_mobile_336x280" style="margin: 5px 0;"></div> <ol><li value="1"><b><strong>State (S):</strong></b><span> The current state of the environment.</span></li><li value="2"><b><strong>Action (A):</strong></b><span> The action taken by the agent in a given state.</span></li><li value="3"><b><strong>Reward (R):</strong></b><span> The immediate reward received after taking action A in state S.</span></li><li value="4"><b><strong>Next State (S’):</strong></b><span> The state resulting from the action A in state S.</span></li><li value="5"><b><strong>Next Action (A’):</strong></b><span> The action chosen in the next state S’ according to the current policy.</span></li></ol><p dir="ltr"><span>The SARSA algorithm updates the Q-value for a given state-action pair based on the reward and the next state-action pair, leading to an updated policy. </span></p> <blockquote><p dir="ltr"><span>SARSA is </span><b><strong>on-policy</strong></b><span>, meaning it learns from the current policy’s actions rather than optimizing actions through a model of the environment.</span></p> </blockquote><h2><b><strong>SARSA Update Rule</strong></b></h2><p dir="ltr"><span>The core idea of SARSA is to update the </span><b><strong>Q-value</strong></b><span> for each state-action pair based on the actual experience (i.e., what the agent does while following its policy). The Q-value is updated using the following </span><a href="https://www.geeksforgeeks.org/bellman-equation/" target="_blank" rel="noopener"><b><strong>Bellman Equation</strong></b></a><span> for SARSA:</span></p> <p dir="ltr" style="text-align: center;">[Tex]Q(s_t, a_t) \leftarrow Q(s_t, a_t) + \alpha \left[ r_{t+1} + \gamma Q(s_{t+1}, a_{t+1}) – Q(s_t, a_t) \right][/Tex]</p> <p dir="ltr"><span>Where:</span></p><div id="GFG_AD_Desktop_InContent_ATF_336x280" style="text-align:center; max-height: 280px;"></div><div id="GFG_AD_gfg_mobile_336x280_1" style="margin: 5px 0;"></div> <ul><li value="1">[Tex]Q(s_t, a_t)[/Tex]<span> is the current Q-value for the state-action pair at time step t.</span></li><li value="2">[Tex]α[/Tex]<span> is the learning rate (a value between 0 and 1), which determines how much the Q-values are updated.</span></li><li value="3">[Tex]r_{t+1}[/Tex]<span> is the reward received after taking action </span>[Tex]a_t[/Tex]<span> in state </span>[Tex]s_t[/Tex]<span>.</span></li><li value="4">[Tex]γ[/Tex]<span> is the discount factor (between 0 and 1), which determines the importance of future rewards.</span></li><li value="5">[Tex]Q(s_{t+1}, a_{t+1})[/Tex]<span> is the Q-value for the next state-action pair.</span></li></ul><p dir="ltr"><span>In this update rule:</span></p><div id="_GFG_ABP_Incontent_728x90" style="text-align:center;"></div> <ul><li value="1"><span>The agent updates its Q-value using both the immediate reward and the expected future reward.</span></li><li value="2"><span>The difference between the predicted Q-value and the actual reward is used to correct the current estimate.</span></li></ul><h4><b><strong>SARSA Algorithm</strong></b></h4><p dir="ltr"><span>Here’s a high-level view of the SARSA algorithm:</span></p> <p dir="ltr"><b><strong>1. Initialize</strong></b><span>:</span></p> <ul><li value="1"><span>Initialize Q-values arbitrarily for each state-action pair.</span></li><li value="2"><span>Choose an initial state </span>[Tex]s_0[/Tex]<span>.</span></li></ul><p dir="ltr"><b><strong>2. Loop for each episode</strong></b><span>:</span></p> <ul><li value="1"><span>Set the initial state </span>[Tex]s_t[/Tex]<span> and choose an action </span>[Tex]a_t[/Tex]<span> based on a policy (e.g., </span>[Tex]\varepsilon[/Tex]<span>-greedy).</span><ul><li value="1"><b><strong>Repeat for each step</strong></b><span> in the episode:</span></li><li value="2"><span>Take action </span>[Tex]a_t[/Tex]<span>, observe reward </span>[Tex]R_{t+1}[/Tex]<span>, and transition to state </span>[Tex]s_{t+1}[/Tex]<span>.</span></li><li value="3"><span>Choose the next action </span>[Tex]a_{t+1}[/Tex]<span> from state </span>[Tex]s_{t+1}[/Tex]<span> based on the policy.</span></li><li value="4"><span>Update the Q-value for the state-action pair </span>[Tex](s_t, a_t)[/Tex]<span> using the SARSA update rule.</span></li><li value="5"><span>Set </span>[Tex]s_t = s_{t+1}[/Tex]<span> and </span>[Tex]a_t = a_{t+1}[/Tex]<span>.</span></li></ul></li><li value="2"><span>Continue until the episode ends (when the agent reaches a terminal state or after a fixed number of steps).</span></li></ul><h2><span>SARSA Implementation in Grid World </span></h2><h3><b><strong>Step 1: Define the Environment (GridWorld)</strong></b></h3><p dir="ltr" style="text-align: start;"><span>The environment is a grid world where the agent can move up, down, left, or right. The environment includes:</span></p> <ul><li value="1"><span>A start position.</span></li><li value="2"><span>A goal position.</span></li><li value="3"><span>Obstacles that the agent should avoid.</span></li><li value="4"><span>A reward structure (e.g., negative reward for hitting obstacles, positive reward for reaching the goal).</span></li></ul><p dir="ltr" style="text-align: start;"><span>The </span><code><span>GridWorld</span></code><span> class handles the environment dynamics, including resetting the state and taking actions.</span></p><div style="text-align:center;margin:20px 0; max-height: 280px;" id="GFG_AD_gfg_outstream_incontent"></div> <gfg-tabs data-run-ide="true" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3" data-main-code-start="None" data-main-code-end="None"> <code class="language-python3"><div class=highlight><pre><span></span><span class=k>class</span> <span class=nc>GridWorld</span><span class=p>:</span> <span class=k>def</span> <span class=fm>__init__</span><span class=p>(</span><span class=bp>self</span><span class=p>,</span> <span class=n>width</span><span class=p>,</span> <span class=n>height</span><span class=p>,</span> <span class=n>start</span><span class=p>,</span> <span class=n>goal</span><span class=p>,</span> <span class=n>obstacles</span><span class=p>):</span> <span class=bp>self</span><span class=o>.</span><span class=n>width</span> <span class=o>=</span> <span class=n>width</span> <span class=bp>self</span><span class=o>.</span><span class=n>height</span> <span class=o>=</span> <span class=n>height</span> <span class=bp>self</span><span class=o>.</span><span class=n>start</span> <span class=o>=</span> <span class=n>start</span> <span class=bp>self</span><span class=o>.</span><span class=n>goal</span> <span class=o>=</span> <span class=n>goal</span> <span class=bp>self</span><span class=o>.</span><span class=n>obstacles</span> <span class=o>=</span> <span class=n>obstacles</span> <span class=bp>self</span><span class=o>.</span><span class=n>state</span> <span class=o>=</span> <span class=n>start</span> <span class=k>def</span> <span class=nf>reset</span><span class=p>(</span><span class=bp>self</span><span class=p>):</span> <span class=bp>self</span><span class=o>.</span><span class=n>state</span> <span class=o>=</span> <span class=bp>self</span><span class=o>.</span><span class=n>start</span> <span class=k>return</span> <span class=bp>self</span><span class=o>.</span><span class=n>state</span> <span class=k>def</span> <span class=nf>step</span><span class=p>(</span><span class=bp>self</span><span class=p>,</span> <span class=n>action</span><span class=p>):</span> <span class=n>x</span><span class=p>,</span> <span class=n>y</span> <span class=o>=</span> <span class=bp>self</span><span class=o>.</span><span class=n>state</span> <span class=k>if</span> <span class=n>action</span> <span class=o>==</span> <span class=mi>0</span><span class=p>:</span> <span class=c1># Up</span> <span class=n>x</span> <span class=o>=</span> <span class=nb>max</span><span class=p>(</span><span class=n>x</span> <span class=o>-</span> <span class=mi>1</span><span class=p>,</span> <span class=mi>0</span><span class=p>)</span> <span class=k>elif</span> <span class=n>action</span> <span class=o>==</span> <span class=mi>1</span><span class=p>:</span> <span class=c1># Down</span> <span class=n>x</span> <span class=o>=</span> <span class=nb>min</span><span class=p>(</span><span class=n>x</span> <span class=o>+</span> <span class=mi>1</span><span class=p>,</span> <span class=bp>self</span><span class=o>.</span><span class=n>height</span> <span class=o>-</span> <span class=mi>1</span><span class=p>)</span> <span class=k>elif</span> <span class=n>action</span> <span class=o>==</span> <span class=mi>2</span><span class=p>:</span> <span class=c1># Left</span> <span class=n>y</span> <span class=o>=</span> <span class=nb>max</span><span class=p>(</span><span class=n>y</span> <span class=o>-</span> <span class=mi>1</span><span class=p>,</span> <span class=mi>0</span><span class=p>)</span> <span class=k>elif</span> <span class=n>action</span> <span class=o>==</span> <span class=mi>3</span><span class=p>:</span> <span class=c1># Right</span> <span class=n>y</span> <span class=o>=</span> <span class=nb>min</span><span class=p>(</span><span class=n>y</span> <span class=o>+</span> <span class=mi>1</span><span class=p>,</span> <span class=bp>self</span><span class=o>.</span><span class=n>width</span> <span class=o>-</span> <span class=mi>1</span><span class=p>)</span> <span class=n>next_state</span> <span class=o>=</span> <span class=p>(</span><span class=n>x</span><span class=p>,</span> <span class=n>y</span><span class=p>)</span> <span class=k>if</span> <span class=n>next_state</span> <span class=ow>in</span> <span class=bp>self</span><span class=o>.</span><span class=n>obstacles</span><span class=p>:</span> <span class=n>reward</span> <span class=o>=</span> <span class=o>-</span><span class=mi>10</span> <span class=n>done</span> <span class=o>=</span> <span class=kc>True</span> <span class=k>elif</span> <span class=n>next_state</span> <span class=o>==</span> <span class=bp>self</span><span class=o>.</span><span class=n>goal</span><span class=p>:</span> <span class=n>reward</span> <span class=o>=</span> <span class=mi>10</span> <span class=n>done</span> <span class=o>=</span> <span class=kc>True</span> <span class=k>else</span><span class=p>:</span> <span class=n>reward</span> <span class=o>=</span> <span class=o>-</span><span class=mi>1</span> <span class=n>done</span> <span class=o>=</span> <span class=kc>False</span> <span class=bp>self</span><span class=o>.</span><span class=n>state</span> <span class=o>=</span> <span class=n>next_state</span> <span class=k>return</span> <span class=n>next_state</span><span class=p>,</span> <span class=n>reward</span><span class=p>,</span> <span class=n>done</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><br></p> <h3><b><strong>Step 2: Define the SARSA Algorithm</strong></b></h3><p dir="ltr"><span>SARSA algorithm iterates over episodes, updating the Q-values based on the agent’s experience.</span></p> <gfg-tabs data-run-ide="true" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3" data-main-code-start="None" data-main-code-end="None"> <code class="language-python3"><div class=highlight><pre><span></span><span class=k>def</span> <span class=nf>sarsa</span><span class=p>(</span><span class=n>env</span><span class=p>,</span> <span class=n>episodes</span><span class=p>,</span> <span class=n>alpha</span><span class=p>,</span> <span class=n>gamma</span><span class=p>,</span> <span class=n>epsilon</span><span class=p>):</span> <span class=c1># Initialize Q-table with zeros</span> <span class=n>Q</span> <span class=o>=</span> <span class=n>np</span><span class=o>.</span><span class=n>zeros</span><span class=p>((</span><span class=n>env</span><span class=o>.</span><span class=n>height</span><span class=p>,</span> <span class=n>env</span><span class=o>.</span><span class=n>width</span><span class=p>,</span> <span class=mi>4</span><span class=p>))</span> <span class=k>for</span> <span class=n>episode</span> <span class=ow>in</span> <span class=nb>range</span><span class=p>(</span><span class=n>episodes</span><span class=p>):</span> <span class=n>state</span> <span class=o>=</span> <span class=n>env</span><span class=o>.</span><span class=n>reset</span><span class=p>()</span> <span class=n>action</span> <span class=o>=</span> <span class=n>epsilon_greedy_policy</span><span class=p>(</span><span class=n>Q</span><span class=p>,</span> <span class=n>state</span><span class=p>,</span> <span class=n>epsilon</span><span class=p>)</span> <span class=n>done</span> <span class=o>=</span> <span class=kc>False</span> <span class=k>while</span> <span class=ow>not</span> <span class=n>done</span><span class=p>:</span> <span class=n>next_state</span><span class=p>,</span> <span class=n>reward</span><span class=p>,</span> <span class=n>done</span> <span class=o>=</span> <span class=n>env</span><span class=o>.</span><span class=n>step</span><span class=p>(</span><span class=n>action</span><span class=p>)</span> <span class=n>next_action</span> <span class=o>=</span> <span class=n>epsilon_greedy_policy</span><span class=p>(</span><span class=n>Q</span><span class=p>,</span> <span class=n>next_state</span><span class=p>,</span> <span class=n>epsilon</span><span class=p>)</span> <span class=c1># SARSA update rule</span> <span class=n>Q</span><span class=p>[</span><span class=n>state</span><span class=p>[</span><span class=mi>0</span><span class=p>],</span> <span class=n>state</span><span class=p>[</span><span class=mi>1</span><span class=p>],</span> <span class=n>action</span><span class=p>]</span> <span class=o>+=</span> <span class=n>alpha</span> <span class=o>*</span> <span class=p>(</span><span class=n>reward</span> <span class=o>+</span> <span class=n>gamma</span> <span class=o>*</span> <span class=n>Q</span><span class=p>[</span><span class=n>next_state</span><span class=p>[</span><span class=mi>0</span><span class=p>],</span> <span class=n>next_state</span><span class=p>[</span><span class=mi>1</span><span class=p>],</span> <span class=n>next_action</span><span class=p>]</span> <span class=o>-</span> <span class=n>Q</span><span class=p>[</span><span class=n>state</span><span class=p>[</span><span class=mi>0</span><span class=p>],</span> <span class=n>state</span><span class=p>[</span><span class=mi>1</span><span class=p>],</span> <span class=n>action</span><span class=p>])</span> <span class=n>state</span> <span class=o>=</span> <span class=n>next_state</span> <span class=n>action</span> <span class=o>=</span> <span class=n>next_action</span> <span class=k>return</span> <span class=n>Q</span> </pre></div></code> </gfg-panel></gfg-tabs><h3><b><strong>Step 3: Define the Epsilon-Greedy Policy</strong></b></h3><p dir="ltr" style="text-align: start;"><span>The epsilon-greedy policy balances exploration and exploitation:</span></p> <ul><li value="1"><span>With probability </span><i><em class="GFGEditorTheme__textItalic">ϵ</em></i><span>, the agent chooses a random action (exploration).</span></li><li value="2"><span>With probability 1−</span><i><em class="GFGEditorTheme__textItalic">ϵ</em></i><span>, the agent chooses the action with the highest Q-value for the current state (exploitation).</span></li></ul><gfg-tabs data-run-ide="true" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3" data-main-code-start="None" data-main-code-end="None"> <code class="language-python3"><div class=highlight><pre><span></span><span class=k>def</span> <span class=nf>epsilon_greedy_policy</span><span class=p>(</span><span class=n>Q</span><span class=p>,</span> <span class=n>state</span><span class=p>,</span> <span class=n>epsilon</span><span class=p>):</span> <span class=k>if</span> <span class=n>random</span><span class=o>.</span><span class=n>uniform</span><span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>1</span><span class=p>)</span> <span class=o><</span> <span class=n>epsilon</span><span class=p>:</span> <span class=k>return</span> <span class=n>random</span><span class=o>.</span><span class=n>randint</span><span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>3</span><span class=p>)</span> <span class=c1># Random action</span> <span class=k>else</span><span class=p>:</span> <span class=k>return</span> <span class=n>np</span><span class=o>.</span><span class=n>argmax</span><span class=p>(</span><span class=n>Q</span><span class=p>[</span><span class=n>state</span><span class=p>[</span><span class=mi>0</span><span class=p>],</span> <span class=n>state</span><span class=p>[</span><span class=mi>1</span><span class=p>]])</span> <span class=c1># Greedy action</span> </pre></div></code> </gfg-panel></gfg-tabs><h3><b><strong>Step 4: Set Up the Environment and Run SARSA</strong></b></h3><p dir="ltr"><span>This step involves:</span></p> <ul><li value="1"><span>Defining the grid world parameters (width, height, start, goal, obstacles).</span></li><li value="2"><span>Setting the SARSA hyperparameters (episodes, learning rate, discount factor, exploration rate).</span></li><li value="3"><span>Running the SARSA algorithm and printing the learned Q-values.</span></li></ul><gfg-tabs data-run-ide="true" data-mode="light"> <gfg-tab slot="tab">Python</gfg-tab> <gfg-panel slot="panel" data-code-lang="python3" data-main-code-start="None" data-main-code-end="None"> <code class="language-python3"><div class=highlight><pre><span></span><span class=k>if</span> <span class=vm>__name__</span> <span class=o>==</span> <span class=s2>"__main__"</span><span class=p>:</span> <span class=c1># Define the grid world environment</span> <span class=n>width</span> <span class=o>=</span> <span class=mi>5</span> <span class=n>height</span> <span class=o>=</span> <span class=mi>5</span> <span class=n>start</span> <span class=o>=</span> <span class=p>(</span><span class=mi>0</span><span class=p>,</span> <span class=mi>0</span><span class=p>)</span> <span class=n>goal</span> <span class=o>=</span> <span class=p>(</span><span class=mi>4</span><span class=p>,</span> <span class=mi>4</span><span class=p>)</span> <span class=n>obstacles</span> <span class=o>=</span> <span class=p>[(</span><span class=mi>2</span><span class=p>,</span> <span class=mi>2</span><span class=p>),</span> <span class=p>(</span><span class=mi>3</span><span class=p>,</span> <span class=mi>2</span><span class=p>)]</span> <span class=n>env</span> <span class=o>=</span> <span class=n>GridWorld</span><span class=p>(</span><span class=n>width</span><span class=p>,</span> <span class=n>height</span><span class=p>,</span> <span class=n>start</span><span class=p>,</span> <span class=n>goal</span><span class=p>,</span> <span class=n>obstacles</span><span class=p>)</span> <span class=c1># SARSA parameters</span> <span class=n>episodes</span> <span class=o>=</span> <span class=mi>1000</span> <span class=n>alpha</span> <span class=o>=</span> <span class=mf>0.1</span> <span class=c1># Learning rate</span> <span class=n>gamma</span> <span class=o>=</span> <span class=mf>0.99</span> <span class=c1># Discount factor</span> <span class=n>epsilon</span> <span class=o>=</span> <span class=mf>0.1</span> <span class=c1># Exploration rate</span> <span class=c1># Run SARSA</span> <span class=n>Q</span> <span class=o>=</span> <span class=n>sarsa</span><span class=p>(</span><span class=n>env</span><span class=p>,</span> <span class=n>episodes</span><span class=p>,</span> <span class=n>alpha</span><span class=p>,</span> <span class=n>gamma</span><span class=p>,</span> <span class=n>epsilon</span><span class=p>)</span> <span class=c1># Print the learned Q-values</span> <span class=nb>print</span><span class=p>(</span><span class=s2>"Learned Q-values:"</span><span class=p>)</span> <span class=nb>print</span><span class=p>(</span><span class=n>Q</span><span class=p>)</span> </pre></div></code> </gfg-panel></gfg-tabs><p dir="ltr"><b><strong>Output: </strong></b></p> <img src="https://media.geeksforgeeks.org/wp-content/uploads/20250226173647290091/Learned-Q-Values.PNG" alt="Learned-Q-Values" width="432" height="531" srcset="https://media.geeksforgeeks.org/wp-content/uploads/20250226173647290091/Learned-Q-Values.PNG 432w,https://media.geeksforgeeks.org/wp-content/uploads/20250226173647290091/Learned-Q-Values-100.PNG 81w,https://media.geeksforgeeks.org/wp-content/uploads/20250226173647290091/Learned-Q-Values-200.PNG 163w,https://media.geeksforgeeks.org/wp-content/uploads/20250226173647290091/Learned-Q-Values-300.PNG 244w"><p dir="ltr" style="text-align: start;"><span>After running the SARSA algorithm, the Q-values represent the expected cumulative reward for each state-action pair. The agent uses these Q-values to make decisions in the environment. Higher Q-values indicate better actions for a given state.</span></p> <h2><b><strong>Exploration Strategies in SARSA</strong></b></h2><p dir="ltr"><span>SARSA typically uses an </span><b><strong>exploration-exploitation strategy</strong></b><span> to choose actions. The most common strategy is </span><b><strong>ε-greedy</strong></b><span>, where:</span></p> <ul><li value="1"><b><strong>Exploitation</strong></b><span>: With probability </span>[Tex]1 – \epsilon[/Tex]<span>, the agent chooses the action with the highest Q-value.</span></li><li value="2"><b><strong>Exploration</strong></b><span>: With probability </span>[Tex]\epsilon[/Tex]<span>, the agent chooses a random action to explore new possibilities.</span></li></ul><p dir="ltr"><span>Over time, </span>[Tex]ϵ[/Tex]<span> is often decayed (reduced) to shift from exploration to exploitation as the agent gains more experience in the environment.</span></p> <h2><b><strong>Strengths of SARSA</strong></b></h2><ol><li value="1"><b><strong>Stability with Exploration</strong></b><span>: SARSA is more stable in environments where the agent needs to balance exploration and exploitation.</span></li><li value="2"><b><strong>On-Policy Learning</strong></b><span>: Since SARSA learns from the policy the agent is actually following, it is better suited for situations where the agent must learn from its exploration.</span></li></ol><h2><b><strong>Weaknesses of SARSA</strong></b></h2><ol><li value="1"><b><strong>Slower Convergence</strong></b><span>: Because SARSA is on-policy, it might take longer to converge compared to off-policy methods like Q-learning, especially when there is a lot of exploration.</span></li><li value="2"><b><strong>Sensitive to the Exploration Strategy</strong></b><span>: The agent’s performance depends on how exploration is managed (e.g., how </span>[Tex]\epsilon[/Tex]<span> is decayed in the ε-greedy strategy).</span></li></ol><p dir="ltr"><span>Understanding SARSA is essential for building RL agents that can effectively learn from their interactions with the environment, especially when an on-policy approach is required.</span></p> <br/><div id="AP_G4GR_6"></div>  <div class='article_bottom_text'></div><br> <div id="video-tab-content" class="video-tab-content"> <div style="text-align: center; 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Boosting builds multiple incremental model</div> </div> <div class="reading-time"> <i class="reading-time-icon" data-gfg-action="readingtime"></i> <span style="color: var(--recommendation-card-text-color);font-size: 14px;font-weight: 500;line-height: 17px;">2 min read</span> </div> </a><hr style="background:var(--gfg-selected-color); width:70%; margin: 0 auto;"></li></ul></div></div></ul> </div> </div> <div class="vote-wrap"> <div style="display:none;align-items:center;justify-content:center;width:100%;"> <button aria-label="like" data-type="like" class="vote-this" style="margin-right: 0; margin-left:0 ;"> <i class="gfg-icon gfg-icon_like favoriteIcon"></i> <span class="favoriteLike">Like</span> <figure class="favoriteText"></figure> </button> </div> </div> </div> <div class="article-meta"> <div class="bottom-wrap"> <div id="GFG_AD_InContent_Desktop_BTF_650x250" style="text-align:center;max-height: 300px;"></div> </div>  </div> </div> </div> </div> <div id="report_modal_content" class="report_modal_content" style="display:hidden;"></div> <div class="onopen-discussion-panel"> <div class="discussion-tab"> <div class="discussion_heading"> <div></div> <i class="gfg-icon close-tab-icon"></i> </div> <div class="discussion_content"> <div style="height:100%"> <div style="height:100%" id="comment-system"></div> </div> </div> </div> </div> <div class="rightBar" style="padding-right: 5px;"> <style> .article--container_content{ align-items: unset !important; 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