deep-rl-course/pr_676/en/unit3/deep-q-network.html

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		<link rel="modulepreload" href="/docs/deep-rl-course/pr_676/en/_app/immutable/chunks/MermaidChart.svelte_svelte_type_style_lang.3fce6c88.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;The Deep Q-Network (DQN)&quot;,&quot;local&quot;:&quot;deep-q-network&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Preprocessing the input and temporal limitation&quot;,&quot;local&quot;:&quot;preprocessing&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"><div class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 max-sm:gap-0.5 h-6 max-sm:h-5 px-2 max-sm:px-1.5 text-[11px] max-sm:text-[9px] font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0"><svg class="w-3 h-3 max-sm:w-2.5 max-sm:h-2.5" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg></span> <span>Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-6 max-sm:h-5 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible w-3 h-3 max-sm:w-2.5 max-sm:h-2.5 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg></button></div> </div>  <h1 class="relative group"><a id="deep-q-network" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#deep-q-network"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>The Deep Q-Network (DQN)</span></h1> <p data-svelte-h="svelte-1jlvrsz">This is the architecture of our Deep Q-Learning network:</p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/deep-q-network.jpg" alt="Deep Q Network"> <p data-svelte-h="svelte-1hypp4p">As input, we take a <strong>stack of 4 frames</strong> passed through the network as a state and output a <strong>vector of Q-values for each possible action at that state</strong>. Then, like with Q-Learning, we just need to use our epsilon-greedy policy to select which action to take.</p> <p data-svelte-h="svelte-1k6czvr">When the Neural Network is initialized, <strong>the Q-value estimation is terrible</strong>. But during training, our Deep Q-Network agent will associate a situation with the appropriate action and <strong>learn to play the game well</strong>.</p>  <h2 class="relative group"><a id="preprocessing" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#preprocessing"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Preprocessing the input and temporal limitation</span></h2> <p data-svelte-h="svelte-1lyhjdo">We need to <strong>preprocess the input</strong>. It’s an essential step since we want to <strong>reduce the complexity of our state to reduce the computation time needed for training</strong>.</p> <p data-svelte-h="svelte-3unddj">To achieve this, we <strong>reduce the state space to 84x84 and grayscale it</strong>. We can do this since the colors in Atari environments don’t add important information.
This is a big improvement since we <strong>reduce our three color channels (RGB) to 1</strong>.</p> <p data-svelte-h="svelte-12atbxn">We can also <strong>crop a part of the screen in some games</strong> if it does not contain important information.
Then we stack four frames together.</p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/preprocessing.jpg" alt="Preprocessing"> <p data-svelte-h="svelte-1d5ylzm"><strong>Why do we stack four frames together?</strong>
We stack frames together because it helps us <strong>handle the problem of temporal limitation</strong>. Let’s take an example with the game of Pong. When you see this frame:</p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/temporal-limitation.jpg" alt="Temporal Limitation"> <p data-svelte-h="svelte-1gpzepb">Can you tell me where the ball is going?
No, because one frame is not enough to have a sense of motion! But what if I add three more frames? <strong>Here you can see that the ball is going to the right</strong>.</p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/temporal-limitation-2.jpg" alt="Temporal Limitation">
That’s why, to capture temporal information, we stack four frames together.
<p data-svelte-h="svelte-1qrirkm">Then the stacked frames are processed by three convolutional layers. These layers <strong>allow us to capture and exploit spatial relationships in images</strong>. But also, because the frames are stacked together, <strong>we can exploit some temporal properties across those frames</strong>.</p> <p data-svelte-h="svelte-7233xh">If you don’t know what convolutional layers are, don’t worry. You can check out <a href="https://www.udacity.com/course/deep-learning-pytorch--ud188" rel="nofollow">Lesson 4 of this free Deep Learning Course by Udacity</a></p> <p data-svelte-h="svelte-2hryy8">Finally, we have a couple of fully connected layers that output a Q-value for each possible action at that state.</p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit4/deep-q-network.jpg" alt="Deep Q Network"> <p data-svelte-h="svelte-2wm1wc">So, we see that Deep Q-Learning uses a neural network to approximate, given a state, the different Q-values for each possible action at that state. Now let’s study the Deep Q-Learning algorithm.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/deep-rl-class/blob/main/units/en/unit3/deep-q-network.mdx" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg> <span data-svelte-h="svelte-zjs2n5"><span class="underline">Update</span> on GitHub</span></a>  <p></p> 
			
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