149 papers with code • 1 benchmarks • 4 datasets
The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.
Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.
The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.
We explore deep reinforcement learning methods for multi-agent domains.
Multi-agent reinforcement learning (MARL) has shown recent success in increasingly complex fixed-team zero-sum environments.
At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted.
At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.