deep-rl-course/pr_587/en/unit5/introduction.html

<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;An Introduction to Unity ML-Agents&quot;,&quot;local&quot;:&quot;introduction-to-ml-agents&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}">
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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;An Introduction to Unity ML-Agents&quot;,&quot;local&quot;:&quot;introduction-to-ml-agents&quot;,&quot;sections&quot;:[],&quot;depth&quot;:1}"><!-- HEAD_svelte-u9bgzb_END -->      <p></p>  <h1 class="relative group"><a id="introduction-to-ml-agents" 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="#introduction-to-ml-agents"><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>An Introduction to Unity ML-Agents</span></h1> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/thumbnail.png" alt="thumbnail"> <p data-svelte-h="svelte-1vcgfrn">One of the challenges in Reinforcement Learning is <strong>creating environments</strong>. Fortunately for us, we can use game engines to do so.
These engines, such as <a href="https://unity.com/" rel="nofollow">Unity</a>, <a href="https://godotengine.org/" rel="nofollow">Godot</a> or <a href="https://www.unrealengine.com/" rel="nofollow">Unreal Engine</a>, are programs made to create video games. They are perfectly suited
for creating environments: they provide physics systems, 2D/3D rendering, and more.</p> <p data-svelte-h="svelte-eyae6a">One of them, <a href="https://unity.com/" rel="nofollow">Unity</a>, created the <a href="https://github.com/Unity-Technologies/ml-agents" rel="nofollow">Unity ML-Agents Toolkit</a>, a plugin based on the game engine Unity that allows us <strong>to use the Unity Game Engine as an environment builder to train agents</strong>. In the first bonus unit, this is what we used to train Huggy to catch a stick!</p> <figure data-svelte-h="svelte-14xtn5a"><img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit5/example-envs.png" alt="MLAgents environments"> <figcaption>Source: <a href="https://github.com/Unity-Technologies/ml-agents">ML-Agents documentation</a></figcaption></figure> <p data-svelte-h="svelte-1d33h02">Unity ML-Agents Toolkit provides many exceptional pre-made environments, from playing football (soccer), learning to walk, and jumping over big walls.</p> <p data-svelte-h="svelte-w4zwn2">In this Unit, we’ll learn to use ML-Agents, but <strong>don’t worry if you don’t know how to use the Unity Game Engine</strong>: you don’t need to use it to train your agents.</p> <p data-svelte-h="svelte-2xi7mi">So, today, we’re going to train two agents:</p> <ul data-svelte-h="svelte-15sbbs0"><li>The first one will learn to <strong>shoot snowballs onto a spawning target</strong>.</li> <li>The second needs to <strong>press a button to spawn a pyramid, then navigate to the pyramid, knock it over, and move to the gold brick at the top</strong>. To do that, it will need to explore its environment, which will be done using a technique called curiosity.</li></ul> <img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit7/envs.png" alt="Environments"> <p data-svelte-h="svelte-14hc6j">Then, after training, <strong>you’ll push the trained agents to the Hugging Face Hub</strong>, and you’ll be able to <strong>visualize them playing directly on your browser without having to use the Unity Editor</strong>.</p> <p data-svelte-h="svelte-11di5c1">Doing this Unit will <strong>prepare you for the next challenge: AI vs. AI where you will train agents in multi-agents environments and compete against your classmates’ agents</strong>.</p> <p data-svelte-h="svelte-1lrk2z4">Sound exciting? Let’s get started!</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/unit5/introduction.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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