Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning
To gain high rewards in muti-agent scenes, it is sometimes necessary to understand other agents and make corresponding optimal decisions.We can solve these tasks by first building models for other agents and then finding the optimal policy with these models.To get an accurate model, many observations are needed and this can be sample-inefficient.Whats more, the learned model and policy can overfit to current agents and cannot generalize if the other agents are replaced by new agents.In many practical situations, each agent we face can be considered as a sample from a population with a fixed but unknown distribution.Thus we can treat the task against some specific agents as a task sampled from a task distribution.We apply meta-learning method to build models and learn policies.Therefore when new agents come, we can adapt to them efficiently.Experiments on grid games show that our method can quickly get high rewards.
Our work applies meta-learning to multi-agent Reinforcement Learning to help our agent efficiently adapted to new coming opponents.This paper focuses on fast adaptation to new behaviour of the other agents of the environment using a method based on MAMLThe paper presents an approach to multi-agent learning based on the framework of model-agnostic meta learning for the task of opponent modeling for multi-agent RL.