Buckets:

hf-doc-build
/

doc-dev

Files

xet

hf-doc-build/doc-dev / deep-rl-course /pr_587 /en /unit1 /two-methods.html

rtrm

about 2 months ago

download

raw

14.1 kB

	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Two main approaches for solving RL problems","local":"two-methods","sections":[{"title":"The Policy π: the agent’s brain","local":"policy","sections":[],"depth":2},{"title":"Policy-Based Methods","local":"policy-based","sections":[],"depth":2},{"title":"Value-based methods","local":"value-based","sections":[],"depth":2}],"depth":1}">
	<link href="/docs/deep-rl-course/pr_587/en/_app/immutable/assets/0.e3b0c442.css" rel="modulepreload">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/entry/start.7c4c7929.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/scheduler.37c15a92.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/singletons.945d9b5d.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/index.18351ede.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/paths.9ffa1e06.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/entry/app.d2a43a08.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/index.7cb9c9b8.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/nodes/0.e67798fa.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/each.e59479a4.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/nodes/19.0e4420cb.js">
	<link rel="modulepreload" href="/docs/deep-rl-course/pr_587/en/_app/immutable/chunks/Tip.d10b3fc9.js">
	<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="{"title":"Two main approaches for solving RL problems","local":"two-methods","sections":[{"title":"The Policy π: the agent’s brain","local":"policy","sections":[],"depth":2},{"title":"Policy-Based Methods","local":"policy-based","sections":[],"depth":2},{"title":"Value-based methods","local":"value-based","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="two-methods" 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="#two-methods"><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>Two main approaches for solving RL problems</span></h1> <div class="course-tip bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">Now that we learned the RL framework, how do we solve the RL problem?</div> <p data-svelte-h="svelte-1poqzkt">In other words, how do we build an RL agent that can <strong>select the actions that maximize its expected cumulative reward?</strong></p> <h2 class="relative group"><a id="policy" 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="#policy"><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 Policy π: the agent’s brain</span></h2> <p data-svelte-h="svelte-1sury9w">The Policy <strong>π</strong> is the <strong>brain of our Agent</strong>, it’s the function that tells us what <strong>action to take given the state we are in.</strong> So it <strong>defines the agent’s behavior</strong> at a given time.</p> <figure data-svelte-h="svelte-wr9ae2"><img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy_1.jpg" alt="Policy"> <figcaption>Think of policy as the brain of our agent, the function that will tell us the action to take given a state</figcaption></figure> <p data-svelte-h="svelte-g271gb">This Policy <strong>is the function we want to learn</strong>, our goal is to find the optimal policy π, the policy that <strong>maximizes expected return</strong> when the agent acts according to it. We find this π <strong>through training.</strong></p> <p data-svelte-h="svelte-7rf0mi">There are two approaches to train our agent to find this optimal policy π*:</p> <ul data-svelte-h="svelte-1dvt8p7"><li><strong>Directly,</strong> by teaching the agent to learn which <strong>action to take,</strong> given the current state: <strong>Policy-Based Methods.</strong></li> <li>Indirectly, <strong>teach the agent to learn which state is more valuable</strong> and then take the action that <strong>leads to the more valuable states</strong>: Value-Based Methods.</li></ul> <h2 class="relative group"><a id="policy-based" 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="#policy-based"><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>Policy-Based Methods</span></h2> <p data-svelte-h="svelte-4zn9b3">In Policy-Based methods, <strong>we learn a policy function directly.</strong></p> <p data-svelte-h="svelte-oozyd0">This function will define a mapping from each state to the best corresponding action. Alternatively, it could define <strong>a probability distribution over the set of possible actions at that state.</strong></p> <figure data-svelte-h="svelte-1m4eqa9"><img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy_2.jpg" alt="Policy"> <figcaption>As we can see here, the policy (deterministic) <b>directly indicates the action to take for each step.</b></figcaption></figure> <p data-svelte-h="svelte-119iy72">We have two types of policies:</p> <ul data-svelte-h="svelte-h86bbg"><li><em>Deterministic</em>: a policy at a given state <strong>will always return the same action.</strong></li></ul> <figure data-svelte-h="svelte-15macrs"><img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy_3.jpg" alt="Policy"> <figcaption>action = policy(state)</figcaption></figure> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy_4.jpg" alt="Policy" width="100%"> <ul data-svelte-h="svelte-1q48317"><li><em>Stochastic</em>: outputs <strong>a probability distribution over actions.</strong></li></ul> <figure data-svelte-h="svelte-dxiuol"><img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy_5.jpg" alt="Policy"> <figcaption>policy(actions \| state) = probability distribution over the set of actions given the current state</figcaption></figure> <figure data-svelte-h="svelte-120mc27"><img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/policy-based.png" alt="Policy Based"> <figcaption>Given an initial state, our stochastic policy will output probability distributions over the possible actions at that state.</figcaption></figure> <p data-svelte-h="svelte-1uwte4">If we recap:</p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/pbm_1.jpg" alt="Pbm recap" width="100%"> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/pbm_2.jpg" alt="Pbm recap" width="100%"> <h2 class="relative group"><a id="value-based" 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="#value-based"><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>Value-based methods</span></h2> <p data-svelte-h="svelte-v48g1w">In value-based methods, instead of learning a policy function, we <strong>learn a value function</strong> that maps a state to the expected value <strong>of being at that state.</strong></p> <p data-svelte-h="svelte-1wvuk6o">The value of a state is the <strong>expected discounted return</strong> the agent can get if it <strong>starts in that state, and then acts according to our policy.</strong></p> <p data-svelte-h="svelte-1xwf190">“Act according to our policy” just means that our policy is <strong>“going to the state with the highest value”.</strong></p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/value_1.jpg" alt="Value based RL" width="100%"> <p data-svelte-h="svelte-19buk39">Here we see that our value function <strong>defined values for each possible state.</strong></p> <figure data-svelte-h="svelte-hm65hv"><img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/value_2.jpg" alt="Value based RL"> <figcaption>Thanks to our value function, at each step our policy will select the state with the biggest value defined by the value function: -7, then -6, then -5 (and so on) to attain the goal.</figcaption></figure> <p data-svelte-h="svelte-4bnt7i">Thanks to our value function, at each step our policy will select the state with the biggest value defined by the value function: -7, then -6, then -5 (and so on) to attain the goal.</p> <p data-svelte-h="svelte-1uwte4">If we recap:</p> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/vbm_1.jpg" alt="Vbm recap" width="100%"> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit1/vbm_2.jpg" alt="Vbm recap" width="100%"> <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/two-methods.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p>

	<script>
	{
	__sveltekit_18oo4fq = {
	assets: "/docs/deep-rl-course/pr_587/en",
	base: "/docs/deep-rl-course/pr_587/en",
	env: {}
	};

	const element = document.currentScript.parentElement;

	const data = [null,null];

	Promise.all([
	import("/docs/deep-rl-course/pr_587/en/_app/immutable/entry/start.7c4c7929.js"),
	import("/docs/deep-rl-course/pr_587/en/_app/immutable/entry/app.d2a43a08.js")
	]).then(([kit, app]) => {
	kit.start(app, element, {
	node_ids: [0, 19],
	data,
	form: null,
	error: null
	});
	});
	}
	</script>

Xet Storage Details

Size:: 14.1 kB
Xet hash:: ef656e56220258270d17f33c5e72504c1825b976ca87bdb133f3e7f2d3c6f612

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.