deep-rl-course/pr_587/en/unit1/summary.html

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		<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/EditOnGithub.858acfec.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;Summary&quot;,&quot;local&quot;:&quot;summary&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="summary" 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="#summary"><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>Summary</span></h1> <p data-svelte-h="svelte-u7ov70">That was a lot of information! Let’s summarize:</p> <ul data-svelte-h="svelte-pq29ug"><li><p>Reinforcement Learning is a computational approach of learning from actions. We build an agent that learns from the environment <strong>by interacting with it through trial and error</strong> and receiving rewards (negative or positive) as feedback.</p></li> <li><p>The goal of any RL agent is to maximize its expected cumulative reward (also called expected return) because RL is based on the <strong>reward hypothesis</strong>, which is that <strong>all goals can be described as the maximization of the expected cumulative reward.</strong></p></li> <li><p>The RL process is a loop that outputs a sequence of <strong>state, action, reward and next state.</strong></p></li> <li><p>To calculate the expected cumulative reward (expected return), we discount the rewards: the rewards that come sooner (at the beginning of the game) <strong>are more probable to happen since they are more predictable than the long term future reward.</strong></p></li> <li><p>To solve an RL problem, you want to <strong>find an optimal policy</strong>. The policy is the “brain” of your agent, which will tell us <strong>what action to take given a state.</strong> The optimal policy is the one which <strong>gives you the actions that maximize the expected return.</strong></p></li> <li><p>There are two ways to find your optimal policy:</p> <ol><li>By training your policy directly: <strong>policy-based methods.</strong></li> <li>By training a value function that tells us the expected return the agent will get at each state and use this function to define our policy: <strong>value-based methods.</strong></li></ol></li> <li><p>Finally, we speak about Deep RL because we introduce <strong>deep neural networks to estimate the action to take (policy-based) or to estimate the value of a state (value-based)</strong> hence the name “deep”.</p></li></ul> <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/unit1/summary.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1">&lt;</span> <span data-svelte-h="svelte-x0xyl0">&gt;</span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a>  <p></p> 
			
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