Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives

Understand

Reinforcement learning agents that operate in diverse and complex environments can benefit from the structured decomposition of their behavior.

• Often, this is addressed in the context of hierarchical reinforcement learning, where the aim is to decompose a policy into lower-level primitives or options, and a higher-level meta-policy that triggers the appropriate behaviors for a given situation.
• However, the meta-policy must still produce appropriate decisions in all states.
• In this work, we propose a policy design that decomposes into primitives, similarly to hierarchical reinforcement learning, but without a high-level meta-policy.