( events) Timezone: America/Los_Angeles
Poster
Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #107
Coordinated Exploration in Concurrent Reinforcement Learning
In
Posters Wed
[
PDF]
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.