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<section class="tex2jax_ignore mathjax_ignore" id="recording-agents">
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<h1>Recording Agents<a class="headerlink" href="#recording-agents" title="Link to this heading">#</a></h1>
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<p>During training or when evaluating an agent, it may be interesting to record agent behaviour over an episode and log the total reward accumulated. This can be achieved through two wrappers: <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordEpisodeStatistics</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordVideo</span></code>, the first tracks episode data such as the total rewards, episode length and time taken and the second generates mp4 videos of the agents using the environment renderings.</p>
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<p>We show how to apply these wrappers for two types of problems; the first for recording data for every episode (normally evaluation) and second for recording data periodiclly (for normal training).</p>
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<section id="recording-every-episode">
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<h2>Recording Every Episode<a class="headerlink" href="#recording-every-episode" title="Link to this heading">#</a></h2>
|
||
<p>Given a trained agent, you may wish to record several episodes during evaluation to see how the agent acts. Below we provide an example script to do this with the <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordEpisodeStatistics</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordVideo</span></code>.</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">gymnasium</span> <span class="k">as</span> <span class="nn">gym</span>
|
||
<span class="kn">from</span> <span class="nn">gymnasium.wrappers</span> <span class="kn">import</span> <span class="n">RecordEpisodeStatistics</span><span class="p">,</span> <span class="n">RecordVideo</span>
|
||
|
||
<span class="n">num_eval_episodes</span> <span class="o">=</span> <span class="mi">4</span>
|
||
|
||
<span class="n">env</span> <span class="o">=</span> <span class="n">gym</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s2">"CartPole-v1"</span><span class="p">,</span> <span class="n">render_mode</span><span class="o">=</span><span class="s2">"rgb_array"</span><span class="p">)</span> <span class="c1"># replace with your environment</span>
|
||
<span class="n">env</span> <span class="o">=</span> <span class="n">RecordVideo</span><span class="p">(</span><span class="n">env</span><span class="p">,</span> <span class="n">video_folder</span><span class="o">=</span><span class="s2">"cartpole-agent"</span><span class="p">,</span> <span class="n">name_prefix</span><span class="o">=</span><span class="s2">"eval"</span><span class="p">,</span>
|
||
<span class="n">episode_trigger</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="kc">True</span><span class="p">)</span>
|
||
<span class="n">env</span> <span class="o">=</span> <span class="n">RecordEpisodeStatistics</span><span class="p">(</span><span class="n">env</span><span class="p">,</span> <span class="n">buffer_length</span><span class="o">=</span><span class="n">num_eval_episodes</span><span class="p">)</span>
|
||
|
||
<span class="k">for</span> <span class="n">episode_num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_eval_episodes</span><span class="p">):</span>
|
||
<span class="n">obs</span><span class="p">,</span> <span class="n">info</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">episode_over</span> <span class="o">=</span> <span class="kc">False</span>
|
||
<span class="k">while</span> <span class="ow">not</span> <span class="n">episode_over</span><span class="p">:</span>
|
||
<span class="n">action</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span> <span class="c1"># replace with actual agent</span>
|
||
<span class="n">obs</span><span class="p">,</span> <span class="n">reward</span><span class="p">,</span> <span class="n">terminated</span><span class="p">,</span> <span class="n">truncated</span><span class="p">,</span> <span class="n">info</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">episode_over</span> <span class="o">=</span> <span class="n">terminated</span> <span class="ow">or</span> <span class="n">truncated</span>
|
||
<span class="n">env</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
|
||
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Episode time taken: </span><span class="si">{</span><span class="n">env</span><span class="o">.</span><span class="n">time_queue</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Episode total rewards: </span><span class="si">{</span><span class="n">env</span><span class="o">.</span><span class="n">return_queue</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Episode lengths: </span><span class="si">{</span><span class="n">env</span><span class="o">.</span><span class="n">length_queue</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
|
||
</pre></div>
|
||
</div>
|
||
<p>In the script above, for the <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordVideo</span></code> wrapper, we specify three different variables: <code class="docutils literal notranslate"><span class="pre">video_folder</span></code> to specify the folder that the videos should be saved (change for your problem), <code class="docutils literal notranslate"><span class="pre">name_prefix</span></code> for the prefix of videos themselves and finally an <code class="docutils literal notranslate"><span class="pre">episode_trigger</span></code> such that every episode is recorded. This means that for every episode of the environment, a video will be recorded and saved in the style “cartpole-agent/eval-episode-x.mp4”.</p>
|
||
<p>For the <code class="xref py py-class docutils literal notranslate"><span class="pre">RecordEpisodicStatistics</span></code>, we only need to specify the buffer lengths, this is the max length of the internal <code class="docutils literal notranslate"><span class="pre">time_queue</span></code>, <code class="docutils literal notranslate"><span class="pre">return_queue</span></code> and <code class="docutils literal notranslate"><span class="pre">length_queue</span></code>. Rather than collect the data for each episode individually, we can use the data queues to print the information at the end of the evaluation.</p>
|
||
<p>For speed ups in evaluating environments, it is possible to implement this with vector environments to in order to evaluate <code class="docutils literal notranslate"><span class="pre">N</span></code> episodes at the same time in parallel rather than series.</p>
|
||
</section>
|
||
<section id="recording-the-agent-during-training">
|
||
<h2>Recording the Agent during Training<a class="headerlink" href="#recording-the-agent-during-training" title="Link to this heading">#</a></h2>
|
||
<p>During training, an agent will act in hundreds or thousands of episodes, therefore, you can’t record a video for each episode, but developers might still want to know how the agent acts at different points in the training, recording episodes periodically during training. While for the episode statistics, it is more helpful to know this data for every episode. The following script provides an example of how to periodically record episodes of an agent while recording every episode’s statistics (we use the python’s logger but <a class="reference external" href="https://www.tensorflow.org/tensorboard">tensorboard</a>, <a class="reference external" href="https://docs.wandb.ai/guides/track">wandb</a> and other modules are available).</p>
|
||
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">logging</span>
|
||
|
||
<span class="kn">import</span> <span class="nn">gymnasium</span> <span class="k">as</span> <span class="nn">gym</span>
|
||
<span class="kn">from</span> <span class="nn">gymnasium.wrappers</span> <span class="kn">import</span> <span class="n">RecordEpisodeStatistics</span><span class="p">,</span> <span class="n">RecordVideo</span>
|
||
|
||
<span class="n">training_period</span> <span class="o">=</span> <span class="mi">250</span> <span class="c1"># record the agent's episode every 250</span>
|
||
<span class="n">num_training_episodes</span> <span class="o">=</span> <span class="mi">10_000</span> <span class="c1"># total number of training episodes</span>
|
||
|
||
<span class="n">env</span> <span class="o">=</span> <span class="n">gym</span><span class="o">.</span><span class="n">make</span><span class="p">(</span><span class="s2">"CartPole-v1"</span><span class="p">,</span> <span class="n">render_mode</span><span class="o">=</span><span class="s2">"rgb_array"</span><span class="p">)</span> <span class="c1"># replace with your environment</span>
|
||
<span class="n">env</span> <span class="o">=</span> <span class="n">RecordVideo</span><span class="p">(</span><span class="n">env</span><span class="p">,</span> <span class="n">video_folder</span><span class="o">=</span><span class="s2">"cartpole-agent"</span><span class="p">,</span> <span class="n">name_prefix</span><span class="o">=</span><span class="s2">"training"</span><span class="p">,</span>
|
||
<span class="n">episode_trigger</span><span class="o">=</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">%</span> <span class="n">training_period</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span>
|
||
<span class="n">env</span> <span class="o">=</span> <span class="n">RecordEpisodeStatistics</span><span class="p">(</span><span class="n">env</span><span class="p">)</span>
|
||
|
||
<span class="k">for</span> <span class="n">episode_num</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_training_episodes</span><span class="p">):</span>
|
||
<span class="n">obs</span><span class="p">,</span> <span class="n">info</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">episode_over</span> <span class="o">=</span> <span class="kc">False</span>
|
||
<span class="k">while</span> <span class="ow">not</span> <span class="n">episode_over</span><span class="p">:</span>
|
||
<span class="n">action</span> <span class="o">=</span> <span class="n">env</span><span class="o">.</span><span class="n">action_space</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span> <span class="c1"># replace with actual agent</span>
|
||
<span class="n">obs</span><span class="p">,</span> <span class="n">reward</span><span class="p">,</span> <span class="n">terminated</span><span class="p">,</span> <span class="n">truncated</span><span class="p">,</span> <span class="n">info</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">episode_over</span> <span class="o">=</span> <span class="n">terminated</span> <span class="ow">or</span> <span class="n">truncated</span>
|
||
|
||
<span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">"episode-</span><span class="si">{</span><span class="n">episode_num</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">info</span><span class="p">[</span><span class="s2">"episode"</span><span class="p">])</span>
|
||
<span class="n">env</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
|
||
</pre></div>
|
||
</div>
|
||
</section>
|
||
<section id="more-information">
|
||
<h2>More information<a class="headerlink" href="#more-information" title="Link to this heading">#</a></h2>
|
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|
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<li><p><a class="reference internal" href="../train_agent/"><span class="std std-doc">Training an agent</span></a></p></li>
|
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<li><p><a class="reference internal" href="#../tutorials/training_agents"><span class="xref myst">More training tutorials</span></a></p></li>
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|
||
"Documentation": [],
|
||
"Repositories": [],
|
||
},
|
||
"Incubating Projects": {
|
||
"Documentation": [],
|
||
"Repositories": [],
|
||
},
|
||
"Foundation": [
|
||
{
|
||
name: "About",
|
||
link: "https://farama.org/about"
|
||
},
|
||
{
|
||
name: "Standards",
|
||
link: "https://farama.org/project_standards",
|
||
},
|
||
{
|
||
name: "Donate",
|
||
link: "https://farama.org/donations"
|
||
}
|
||
]
|
||
}
|
||
|
||
// Categorize projects
|
||
Object.keys(jsonResponse).forEach(key => {
|
||
projectJson = jsonResponse[key];
|
||
if (projectJson.website !== null) {
|
||
projectJson.link = projectJson.website;
|
||
} else {
|
||
projectJson.link = projectJson.github;
|
||
}
|
||
if (projectJson.type === "core") {
|
||
sections["Core Projects"].push(projectJson)
|
||
} else if (projectJson.type == "mature") {
|
||
if (projectJson.website !== null) {
|
||
sections["Mature Projects"]["Documentation"].push(projectJson)
|
||
} else {
|
||
sections["Mature Projects"]["Repositories"].push(projectJson)
|
||
}
|
||
} else {
|
||
if (projectJson.website !== null) {
|
||
sections["Incubating Projects"]["Documentation"].push(projectJson)
|
||
} else {
|
||
sections["Incubating Projects"]["Repositories"].push(projectJson)
|
||
}
|
||
}
|
||
})
|
||
|
||
const menuContainer = document.querySelector(".farama-header-menu__body");
|
||
|
||
Object.keys(sections).forEach((key, i) => {
|
||
const sectionElem = Object.assign(
|
||
document.createElement('div'), {
|
||
className:'farama-header-menu__section',
|
||
}
|
||
)
|
||
sectionElem.appendChild(Object.assign(document.createElement('span'),
|
||
{
|
||
className:'farama-header-menu__section-title' ,
|
||
innerText: key
|
||
}
|
||
))
|
||
// is not a list
|
||
if (sections[key].constructor !== Array) {
|
||
const subSections = sections[key];
|
||
const subSectionContainerElem = Object.assign(
|
||
document.createElement('div'), {
|
||
className:'farama-header-menu__subsections-container',
|
||
style: 'display: flex'
|
||
}
|
||
)
|
||
Object.keys(subSections).forEach((subKey, i) => {
|
||
const subSectionElem = Object.assign(
|
||
document.createElement('div'), {
|
||
className:'farama-header-menu__subsection',
|
||
}
|
||
)
|
||
subSectionElem.appendChild(Object.assign(document.createElement('span'),
|
||
{
|
||
className:'farama-header-menu__subsection-title' ,
|
||
innerText: subKey
|
||
}
|
||
))
|
||
const ulElem = createProjectsList(subSections[subKey], key !== 'Foundation');
|
||
subSectionElem.appendChild(ulElem);
|
||
subSectionContainerElem.appendChild(subSectionElem);
|
||
})
|
||
sectionElem.appendChild(subSectionContainerElem);
|
||
} else {
|
||
const projects = sections[key];
|
||
const ulElem = createProjectsList(projects, true);
|
||
sectionElem.appendChild(ulElem);
|
||
}
|
||
menuContainer.appendChild(sectionElem)
|
||
});
|
||
}
|
||
|
||
xhr.onerror = function() {
|
||
console.error("Unable to load projects");
|
||
};
|
||
|
||
xhr.send();
|
||
</script>
|
||
|
||
|
||
<script>
|
||
const versioningConfig = {
|
||
githubUser: 'Farama-Foundation',
|
||
githubRepo: 'Gymnasium',
|
||
};
|
||
fetch('/main/_static/versioning/versioning_menu.html').then(response => {
|
||
if (response.status === 200) {
|
||
response.text().then(text => {
|
||
const container = document.createElement("div");
|
||
container.innerHTML = text;
|
||
document.querySelector("body").appendChild(container);
|
||
// innerHtml doenst evaluate scripts, we need to add them dynamically
|
||
Array.from(container.querySelectorAll("script")).forEach(oldScript => {
|
||
const newScript = document.createElement("script");
|
||
Array.from(oldScript.attributes).forEach(attr => newScript.setAttribute(attr.name, attr.value));
|
||
newScript.appendChild(document.createTextNode(oldScript.innerHTML));
|
||
oldScript.parentNode.replaceChild(newScript, oldScript);
|
||
});
|
||
});
|
||
} else {
|
||
console.warn("Unable to load versioning menu", response);
|
||
}
|
||
});
|
||
</script>
|
||
|
||
</body>
|
||
</html> |