Coordinated Exploration in Concurrent Reinforcement Learning
  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.
