Multi-agent Reinforcement Learning
223 papers with code • 2 benchmarks • 6 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.
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.
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.
This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility.
Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems.