Coordinated Exploration in Concurrent Reinforcement Learning

2018-02-05 · ICML 2018 7 · Maria Dimakopoulou, Benjamin Van Roy

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.

📄 PDF Abstract BibTeX arXiv:1802.01282

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