A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning
This work addresses a specific bottleneck in reinforcement learning for researchers and practitioners, though it appears incremental.
The paper tackles the inefficiency of replay memory in deep reinforcement learning by proposing a dual memory structure, achieving higher training and test scores than conventional single memory in three OpenAI Gym environments.
In this paper, we propose a dual memory structure for reinforcement learning algorithms with replay memory. The dual memory consists of a main memory that stores various data and a cache memory that manages the data and trains the reinforcement learning agent efficiently. Experimental results show that the dual memory structure achieves higher training and test scores than the conventional single memory structure in three selected environments of OpenAI Gym. This implies that the dual memory structure enables better and more efficient training than the single memory structure.