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spectral-rl2

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Representation Learning (RepL) Methods in Reinforcement Learning and Causal Inference

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# **Note:** For an actively maintained and up-to-date implementation of spectral representation–based RL algorithms, please refer to our new library: [spectral-rl](https://github.com/spectral-rl/spectral-rl).

## Overview
This repo is dedicated to exploring the field of Representation Learning (RepL) with a specific focus on Reinforcement Learning (RL) and Causal Inference. Our goal is to build a comprehensive resource that integrates our latest research and practical implementations.

[Website] [RL-REP: Representation-based Reinforcement Learning](https://haotiansun14.github.io/rl-rep-page/)

## Representation-based Reinforcement Learning
This repo contains implementations for RL with:
- Latent Variable Representations (LV), as outlined in [1].
- Contrastive Representations (CTRL), as described in [2].
- Multi-step Latent Variable Representation $\mu \textit{LV-Rep}$, as described in [3].
- Diffusion Spectral Representation (Diff-SR), as described in [4].
- Spectral Decomposition Representation (SPEDER), as described in [5].

## Directory
- `agent` hosts implementation files for various agents, including the Soft Actor-Critic baseline (`sac`), SAC with Latent Variable (`vlsac`), SAC with Contrastive Representations (`ctrlsac`), and DrQv2 with Multi-step Latent Variable Representation (`mulvdrq`), SAC with Diff-SR (`diffsrsac`), SAC with SPEDER (`spedersac`).
- `networks` contains base implementations for critics, policy networks, variational autoencoders (VAE), and more.
- `utils` comprises replay buffers and several auxiliary functions.

## Run
Execute the `main.py` script with your preferred arguments, such as `--alg` for algorithm type, `--env` for environment, and so on.

Example usage: `python main.py --alg vlsac --env HalfCheetah-v3`.

## References
[1] [Ren, Tongzheng, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, and Bo Dai. "Latent variable representation for reinforcement learning." arXiv preprint arXiv:2212.08765 (2022).](https://arxiv.org/abs/2212.08765)

[2] [Zhang, Tianjun, Tongzheng Ren, Mengjiao Yang, Joseph Gonzalez, Dale Schuurmans, and Bo Dai. "Making linear mdps practical via contrastive representation learning." In International Conference on Machine Learning, pp. 26447-26466. PMLR, 2022.](https://arxiv.org/abs/2207.07150)

[3] [Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, and Bo Dai. "Provable Representation with Efficient Planning for Partial Observable Reinforcement Learning." arXiv preprint arXiv:2311.12244 (2024).](https://arxiv.org/abs/2311.12244)

[4] [Dmitry Shribak and Chen-Xiao Gao and Yitong Li and Chenjun Xiao and Bo Dai. "Diffusion Spectral Representation for Reinforcement Learning." arXiv preprint arXiv:2406.16121 (2024).](https://arxiv.org/abs/2406.16121)

[5] [Tongzheng Ren and Tianjun Zhang and Lisa Lee and Joseph E. Gonzalez and Dale Schuurmans and Bo Dai. "Spectral Decomposition Representation for Reinforcement Learning." arXiv preprint arXiv:2208.09515 (2023).](https://arxiv.org/abs/2208.09515)

If you find our work helpful, please consider citing our paper:
```
@misc{ren2023latent,
      title={Latent Variable Representation for Reinforcement Learning}, 
      author={Tongzheng Ren and Chenjun Xiao and Tianjun Zhang and Na Li and Zhaoran Wang and Sujay Sanghavi and Dale Schuurmans and Bo Dai},
      year={2023},
      eprint={2212.08765},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```
```
@misc{zhang2022making,
      title={Making Linear MDPs Practical via Contrastive Representation Learning}, 
      author={Tianjun Zhang and Tongzheng Ren and Mengjiao Yang and Joseph E. Gonzalez and Dale Schuurmans and Bo Dai},
      year={2022},
      eprint={2207.07150},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```
```
@misc{zhang2024provable,
      title={Provable Representation with Efficient Planning for Partially Observable Reinforcement Learning},
      author={Hongming Zhang and Tongzheng Ren and Chenjun Xiao and Dale Schuurmans and Bo Dai},
      year={2024},
      eprint={2311.12244},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
```
```
@misc{shribak2024diffusionspectralrepresentationreinforcement,
      title={Diffusion Spectral Representation for Reinforcement Learning}, 
      author={Dmitry Shribak and Chen-Xiao Gao and Yitong Li and Chenjun Xiao and Bo Dai},
      year={2024},
      eprint={2406.16121},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2406.16121}, 
}
```
```
@misc{ren2023spectraldecompositionrepresentationreinforcement,
      title={Spectral Decomposition Representation for Reinforcement Learning}, 
      author={Tongzheng Ren and Tianjun Zhang and Lisa Lee and Joseph E. Gonzalez and Dale Schuurmans and Bo Dai},
      year={2023},
      eprint={2208.09515},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2208.09515}, 
}
```

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