🔋 Datasets with baselines for offline multi-agent reinforcement learning.
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Updated
Jul 6, 2024 - Python
🔋 Datasets with baselines for offline multi-agent reinforcement learning.
PyTorch Implementation of MOPO
Code for Tackling Long-Horizon Tasks with Model-based Offline Reinforcement Learning
A Production Tool for Embodied AI
[ICLR 2024] The official implementation of "Safe Offline Reinforcement Learning with Feasibility-Guided Diffusion Model"
Codebase for the paper "Efficient Offline Reinforcement Learning: The Critic is Critical"
A collection of offline reinforcement learning algorithms.
A Japanese (Riichi) Mahjong AI Framework
The source code to Cross-Validated Off-Policy Evaluation
Original implementations of the VC-FB and MC-FB algorithms from "Zero-Shot Reinforcement Learning from Low Quality Data" by Jeen et. al (2024).
PyTorch implementation of the Offline Reinforcement Learning algorithm CQL. Includes the versions DQN-CQL and SAC-CQL for discrete and continuous action spaces.
An elegant PyTorch offline reinforcement learning library for researchers.
[NeurIPS 2023] The official implementation of "Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization"
Robust Offline Reinforcement Learning with Heavy-Tailed Rewards
The Official Code for Offline Model-based Adaptable Policy Learning (NeurIPS'21 & TPAMI)
Code for FOCAL Paper Published at ICLR 2021
Large-Scale and Comprehensive Data Hub for Reinforcement Learning
Code for NeurIPS 2022 paper Exploiting Reward Shifting in Value-Based Deep RL
Official implementation of "Direct Preference-based Policy Optimization without Reward Modeling" (NeurIPS 2023)
Benchmark for "Offline Policy Comparison with Confidence"
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