Retrieval-Augmented Reinforcement Learning
The paper introduces a Retrieval-Augmented Agent (R2A) to overcome limitations in deep reinforcement learning where model capacity is insufficient. Instead of relying solely on learned policies, R2A augments an agent with a retrieval process that accesses past experiences directly. This retrieval mechanism provides relevant contextual information to the agent at each step, significantly improving performance and sample efficiency in complex environments.