Stars
Learning human-aware robot navigation behavior from demonstrations via Maximum Entropy Inverse Reinforcement Learning.
Code for the RL method MATD3 described in the paper "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics"
Clean implementation of Multi-Agent Reinforcement Learning methods (MADDPG, MATD3, MASAC, MAD4PG) in TensorFlow 2.x
Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019
Implementation of CoDAIL in the ICLR 2020 paper <Multi-Agent Interactions Modeling with Correlated Policies>
ICLR 2020 - Multi-Agent Interactions Learning with Correlated Policies Modeling
PyTorch implementation of GAIL and AIRL based on PPO.
Multi-Agent Adversarial Inverse Reinforcement Learning, ICML 2019.
PyTorch Implementation of MADDPG (Lowe et. al. 2017)
Implementation of Multi-Agent Reinforcement Learning algorithm(s). Currently includes: MADDPG
(Experimental) Inverse reinforcement learning from trajectories generated by multiple agents with different (but correlated) rewards
Maximum Causal Entropy Inverse Reinforcement Learning
A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)
Inverse Reinforcement Learning via State Marginal Matching, CoRL 2020
Inverse RL algorithms (APP, MaxEnt, GAIL, VAIL)
Python implementation of a bunch of multi-robot path-planning algorithms.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
PyTorch公式チュートリアル（日本語翻訳版）の各ノートブックファイル（Google Colab用）です
Implementations of selected inverse reinforcement learning algorithms.
Tensorflow implementation of generative adversarial imitation learning
An index of algorithms for offline reinforcement learning (offline-rl)
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
A Pytorch implementation of the multi agent deep deterministic policy gradients (MADDPG) algorithm