Adaptive state-action abstractions via rate-distortion
Researchers have developed a new principle for reinforcement learning that allows agents to dynamically adjust the granularity of their task abstractions during learning. This method refines abstractions when the learning error within them approaches the error introduced by the abstraction itself. The approach, formalized using a performance certificate and implemented with soft state-action abstractions based on rate-distortion principles, has been validated in tabular settings, demonstrating near-optimal performance even with significant state and action information compression. AI
IMPACT Introduces a novel method for adaptive abstraction in RL, potentially improving learning efficiency and performance in complex tasks.