Paper list of multi-agent reinforcement learning (MARL)
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Updated
Jul 18, 2024
Paper list of multi-agent reinforcement learning (MARL)
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
For deep RL and the future of AI.
A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
A pytorch implementation of MADDPG (multi-agent deep deterministic policy gradient)
A suite of test scenarios for multi-agent reinforcement learning.
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
Deep & Classical Reinforcement Learning + Machine Learning Examples in Python
Multi-Agent Reinforcement Learning (MARL) papers with code
A selection of state-of-the-art research materials on decision making and motion planning.
Multi-Agent Reinforcement Learning (MARL) papers
A Collection of Multi-Agent Reinforcement Learning (MARL) Resources
Lightweight multi-agent gridworld Gym environment
🏆 gym-cooking: Code for "Too many cooks: Bayesian inference for coordinating multi-agent collaboration", Winner of the CogSci 2020 Computational Modeling Prize in High Cognition, and a NeurIPS 2020 CoopAI Workshop Best Paper.
[CoRL 2020] Learning a Decentralized Multiarm Motion Planner
some Multiagent enviroment in 《Multi-agent Reinforcement Learning in Sequential Social Dilemmas》 and 《Value-Decomposition Networks For Cooperative Multi-Agent Learning》
PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.
We extend pymarl2 to pymarl3, equipping the MARL algorithms with permutation invariance and permutation equivariance properties. The enhanced algorithm achieves 100% win rates on SMAC-V1 and superior performance on SMAC-V2.
This is a framework for the research on multi-agent reinforcement learning and the implementation of the experiments in the paper titled by ''Shapley Q-value: A Local Reward Approach to Solve Global Reward Games''.
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