# Offline and Offline to Online RL algorithm's implementations This repository includes the implementation (in [Pytorch](https://pytorch.org/docs/stable/torch.html)) for the common offline RL baselines and Offline-Online RL algorithms too. The code (and coding style) is mainly inspired by the [CORL](https://github.com/corl-team/CORL). Highly recommend to check CORL repo too. The repo contains many folder that include the implementation code for each algorithms. To run the code, please follow the instruction below. # Install First, create the conda environment with ```python==3.9.16``` ``` conda create -n off_offon python=3.9.16 ``` Then, please download and install ```mujoco=2.1```, and setup follow [this](https://github.com/openai/mujoco-py) intruction. Finally, install all the dependences ``` conda activate off_offon pip install -r requirements.txt ``` # List of the algorithms - [AWAC](). - [IQL](). - [SQL](). - [EQL](). - [INAC](). # References - [AWAC: Accelerating Online Reinforcement Learning with Offline Datasets](https://arxiv.org/abs/2006.09359) - Ashvin Nair, Abhishek Gupta, Murtaza Dalal, Sergey Levine. 2020. - [Offline Reinforcement Learning with Implicit Q-Learning](https://arxiv.org/abs/2110.06169) - Ilya Kostrikov, Ashvin Nair, Sergey Levine. 2021. - [The In-Sample Softmax for Offline Reinforcement Learning](https://arxiv.org/abs/2302.14372) - Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, Martha White. 2023. - [Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization](https://arxiv.org/abs/2303.15810) - Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, Xianyuan Zhan. 2023.