Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning
Solving tasks with sparse rewards is one of the most important challenges in reinforcement learning. In the single-agent setting, this challenge is addressed by introducing intrinsic rewards that motivate agents to explore unseen regions of their state spaces; however, applying these techniques naively to the multi-agent setting results in agents exploring independently, without any coordination among themselves. We argue that exploration in cooperative multi-agent settings can be accelerated and improved if agents coordinate with respect to the regions of the state space they explore. In this paper we propose an approach for learning how to dynamically select between proposed intrinsic reward types which consider not just what an individual agent has explored, but all agents, such that the agents can coordinate their exploration and maximize extrinsic returns. Concretely, we formulate the approach as a hierarchical policy where a high-level controller selects among sets of policies trained on diverse intrinsic rewards and the low-level controllers learn the action policies of all agents under these specific rewards. We demonstrate the effectiveness of the proposed approach in a multi-agent GridWorld domain with sparse rewards and then show that our method scales up to more complex settings by evaluating on the VizDoom platform.