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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Transition Occupancy Matching (TOM) is a policy-aware model reinforcement learning algorithm that achieves high sample-efficiency and asymptotic performance.">
<meta name="keywords" content="model-based reinforcement learning, dual reinforcement learning">
<meta property="og:image" content="./static/images/peg_preview.jpg" />
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<title>TOM</title>
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<h1 class="title is-1 publication-title">
TOM: Learning Policy-Aware Models for Model-Based Reinforcement Learning via Transition Occupancy Matching </h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://www.seas.upenn.edu/~jasonyma/">Jason Ma*</a>,</span>
<span class="author-block">
<a href="https://kausiksivakumar.github.io/">Kausik Sivakumar*</a>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/jasyan/">Jason Yan</a>,</span>
<span class="author-block">
<a href="https://obastani.github.io/">Osbert Bastani</a>,</span>
<span class="author-block">
<a href="https://www.seas.upenn.edu/~dineshj/">Dinesh Jayaraman</a>,</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">University of Pennsylvania, L4DC 2023</span>
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<h2 style="text-align:left" class="title is-3">Abstract</h2>
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<p>
<b>Standard model-based reinforcement learning (MBRL) approaches fit a transition model of the environment to all past experience, but this wastes model capacity on data that is irrelevant for policy improvement. We instead propose a new "transition occupancy matching" (TOM) objective for MBRL model learning: a model is good to the extent that the current policy experiences the same distribution of transitions inside the model as in the real environment. We derive TOM directly from a novel lower bound on the standard reinforcement learning objective. To optimize TOM, we show how to reduce it to a form of importance weighted maximum-likelihood estimation, where the automatically computed importance weights identify policy-relevant past experiences from a replay buffer, enabling stable optimization. TOM thus offers a plug-and-play model learning sub-routine that is compatible with any backbone MBRL algorithm. On various Mujoco continuous robotic control tasks, we show that TOM successfully focuses model learning on policy-relevant experience and drives policies faster to higher task rewards than alternative model learning approaches.</b>
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<h2 style="text-align:left" class="title is-3">Exploration</h2>
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<p>
<span class="method">PEG</span>'s superior evaluation performance is attributed to its sophisticated exploration, which enables the agent to learn from more informative data. PEG learns complex skills like cartwheeling in the walker environment, obstacle navigation in the ant maze, and stacking in the block environment all through an unsupervised exploration objective.
</p>
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<video id="matting-video" autoplay muted loop controls playsinline width="100%">
<source src="./static/videos/peg_exploration_row.mp4"
type="video/mp4">
</video>
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</section> -->
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{
hu2023planning,
title={Planning Goals for Exploration},
author={Edward S. Hu and Richard Chang and Oleh Rybkin and Dinesh Jayaraman},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=6qeBuZSo7Pr}
}</code></pre>
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