Coordinated Exploration in Concurrent Reinforcement Learning

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Maria Dimakopoulou, Benjamin Van Roy ;
Proceedings of the 35th International Conference on Machine Learning, PMLR 80:1271-1279, 2018.

Abstract

We consider a team of reinforcement learning agents that concurrently learn to operate in a common environment. We identify three properties - adaptivity, commitment, and diversity - which are necessary for efficient coordinated exploration and demonstrate that straightforward extensions to single-agent optimistic and posterior sampling approaches fail to satisfy them. As an alternative, we propose seed sampling, which extends posterior sampling in a manner that meets these requirements. Simulation results investigate how per-agent regret decreases as the number of agents grows, establishing substantial advantages of seed sampling over alternative exploration schemes.

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