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Awesome Model-Based Reinforcement Learning

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This is a collection of research papers for model-based reinforcement learning (mbrl). And the repository will be continuously updated to track the frontier of model-based rl.

Welcome to follow and star!

[2024.10.27] New: We update the NeurIPS 2024 paper list of model-based rl!

[2024.05.20] We update the ICML 2024 paper list of model-based rl.

[2023.11.29] We update the ICLR 2024 paper list of model-based rl.

[2023.09.29] We update the NeurIPS 2023 paper list of model-based rl.

[2023.06.15] We update the ICML 2023 paper list of model-based rl.

[2023.02.05] We update the ICLR 2023 paper list of model-based rl.

[2022.11.03] We update the NeurIPS 2022 paper list of model-based rl.

[2022.07.06] We update the ICML 2022 paper list of model-based rl.

[2022.02.13] We update the ICLR 2022 paper list of model-based rl.

[2021.12.28] We release the awesome model-based rl.

Table of Contents

A Taxonomy of Model-Based RL Algorithms

We’ll start this section with a disclaimer: it’s really quite hard to draw an accurate, all-encompassing taxonomy of algorithms in the Model-Based RL space, because the modularity of algorithms is not well-represented by a tree structure. So we will publish a series of related blogs to explain more Model-Based RL algorithms.


A non-exhaustive, but useful taxonomy of algorithms in modern Model-Based RL.

We simply divide Model-Based RL into two categories: Learn the Model and Given the Model.

  • Learn the Model mainly focuses on how to build the environment model.

  • Given the Model cares about how to utilize the learned model.

And we give some examples as shown in the figure above. There are links to algorithms in taxonomy.

[1] World Models: Ha and Schmidhuber, 2018
[2] I2A (Imagination-Augmented Agents): Weber et al, 2017
[3] MBMF (Model-Based RL with Model-Free Fine-Tuning): Nagabandi et al, 2017
[4] MBVE (Model-Based Value Expansion): Feinberg et al, 2018
[5] ExIt (Expert Iteration): Anthony et al, 2017
[6] AlphaZero: Silver et al, 2017
[7] POPLIN (Model-Based Policy Planning): Wang et al, 2019
[8] M2AC (Masked Model-based Actor-Critic): Pan et al, 2020

Papers

format:
- [title](paper link) [links]
  - author1, author2, and author3
  - Key: key problems and insights
  - OpenReview: optional
  - ExpEnv: experiment environments

Classic Model-Based RL Papers

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NeurIPS 2024

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ICML 2024

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ICLR 2024

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NeurIPS 2023

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ICML 2023

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ICLR 2023

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NeurIPS 2022

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ICML 2022

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ICLR 2022

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NeurIPS 2021

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ICLR 2021

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ICML 2021

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Other

Tutorial

Codebase

  • mbrl-lib - Meta: Library for Model Based RL
  • DI-engine - OpenDILab: Decision AI Engine

Contributing

Our purpose is to make this repo even better. If you are interested in contributing, please refer to HERE for instructions in contribution.

License

Awesome Model-Based RL is released under the Apache 2.0 license.

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