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

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].

• 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).
• networks contains base implementations for critics, policy networks, variational autoencoders (VAE), and more.
• utils comprises replay buffers and several auxiliary functions.

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.

[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).

[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.

[3] Hongming Zhang, Tongzheng Ren, Chenjun Xiao, Dale Schuurmans, and Bo Dai. "Efficient Reinforcement Learning from Partial Observability." arXiv preprint arXiv:2311.12244 (2024).

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{zhang2024efficient,
      title={Efficient Reinforcement Learning from Partial Observability}, 
      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}
}