High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC
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Updated
Aug 3, 2023 - Python
High-quality single-file implementations of SOTA Offline and Offline-to-Online RL algorithms: AWAC, BC, CQL, DT, EDAC, IQL, SAC-N, TD3+BC, LB-SAC, SPOT, Cal-QL, ReBRAC
A collection of robotics simulation environments for reinforcement learning
Clean single-file implementation of offline RL algorithms in JAX
Unified Implementations of Offline Reinforcement Learning Algorithms
Single-file SAC-N implementation on jax with flax and equinox. 10x faster than pytorch
Codes accompanying the paper "Score Regularized Policy Optimization through Diffusion Behavior" (ICLR 2024).
PyTorch Implementation of Offline Reinforcement Learning algorithms
Non-modular implementation of common RL algorithms
Learning from Sparse Offline Datasets via Conservative Density Estimation (ICLR 2024)
a clear and fast jax/flax version of [Diffusion-Policies-for-Offline-RL](https://github.com/Zhendong-Wang/Diffusion-Policies-for-Offline-RL)
🌟 Align diffusion processes with detailed human preferences to improve machine learning models for richer, more accurate outputs.
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