Paper list of multi-agent reinforcement learning (MARL)

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)

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

some Multiagent enviroment in 《Multi-agent Reinforcement Learning in Sequential Social Dilemmas》 and 《Value-Decomposition Networks For Cooperative Multi-Agent Learning》

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''.

A selection of state-of-the-art research materials on decision making and motion planning.

The Reinforcement-Learning-Related Papers of ICLR 2019

A collection of multi-agent reinforcement learning OpenAI gym environments

Lightweight multi-agent gridworld Gym environment

Implementation of Multi-Agent Reinforcement Learning algorithm(s). Currently includes: MADDPG

MADRL, Reinforcement Learning, Multi-Agent, MARL, Communication, Centralized Training and Decentralized Execution

This is Multi agent deep reinforcement learning repo which trains an agent to play Tennis. It trains by playing against itself.

Slither-in Inspired Snake Environment for OpenAI Gym (Part of Requests for Research 2.0)

Emergence of complex strategies through multiagent competition

Implementation of Q-Learning using TD error to navigate a maze avoiding obstacles and a moving enemy

A pytorch implementation of Structured Exploration via Deep Hierarchical Coordination

An OpenAI gym multi-agent environment implementing the Commons Game proposed in "A multi-agent reinforcement learning model of common-pool resource appropriation"

EE485 research project

Code for the RL method MATD3 described in the paper "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics"

Communicative Multiagent Deep Reinforcement Learning for Anatomical Landmark Detection using PyTorch.

Total War Battle simulator for AI research

deep multi agent reinforcement learning tutorial book for intermediate

Learning Grounded Language via Split Screen Communication Learning via Deep Multi-Agent Reinforcement Learning

Board-and-card games are those which involve higher level of uncertainty as it includes the probability of getting the right card and the moves made by other players. We look to model such games as Markov Games and find an optimal policy through the Minimax – Q algorithm. This will also be a test for the Minimax – Q algorithm to check how it performs in a situation with multiple goal states.

Intern project, improving on the original publication

Implementation of the DDPG algorithm to solve Continuous Control Reacher Environment