An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning

TL;DR

This paper introduces a novel multiagent transfer learning framework that improves MARL efficiency by modeling policy transfer as option learning and addressing partial observability challenges.

Contribution

It proposes MAPTF, a framework that learns when and which agent's policy to transfer, and introduces successor representation option learning to enhance accuracy under partial observability.

Findings

Significantly boosts MARL performance in discrete and continuous spaces.

Effectively models policy transfer as option learning.

Addresses partial observability with successor representation.

Abstract

Transfer Learning has shown great potential to enhance single-agent Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents can share knowledge with each other. However, it remains a problem of how an agent should learn from other agents. In this paper, we propose a novel Multiagent Policy Transfer Framework (MAPTF) to improve MARL efficiency. MAPTF learns which agent's policy is the best to reuse for each agent and when to terminate it by modeling multiagent policy transfer as the option learning problem. Furthermore, in practice, the option module can only collect all agent's local experiences for update due to the partial observability of the environment. While in this setting, each agent's experience may be inconsistent with each other, which may cause the inaccuracy and oscillation of the option-value's estimation. Therefore, we propose a novel option learning algorithm, the successor representation option learning to solve it by decoupling the environment dynamics from rewards and learning the option-value under each agent's preference. MAPTF can be easily combined with existing deep RL and MARL approaches, and experimental results show it significantly boosts the performance of existing methods in both discrete and continuous state spaces.