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In a multi-agent setting, the optimal policy of a single agent is largelydependent on the behavior of other agents. We investigate the problem ofmulti-agent reinforcement learning, focusing on decentralized learning innon-stationary domains for mobile robot navigation. We identify a cause for thedifficulty in training non-stationary policies: mutual adaptation tosub-optimal behaviors, and we use this to motivate a curriculum-based strategyfor learning interactive policies. The curriculum has two stages. First, theagent leverages policy gradient algorithms to learn a policy that is capable ofachieving multiple goals. Second, the agent learns a modifier policy to learnhow to interact with other agents in a multi-agent setting. We evaluated ourapproach on both an autonomous driving lane-change domain and a robotnavigation domain.

Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals