A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning

Replay memory plays an important role in stable learning and fast convergence of deep reinforcement learning algorithms [1] that are methods of approximating a value or a policy function using deep neural networks [2]. The study of replay memory in reinforcement learning started from [3] and played a major role in training reinforcement learning agents to play Atari 2600 games with a Deep Q-Network (DQN) [4]. In addition, replay memory is used in other off-policy reinforcement learning algorithms such as DDPG [5] and ACER [6]. In [7], after analyzing the importance of the data in the replay memory, a probability distribution is assigned to enable efficient learning through prioritization based on the Figure 1: Proposed dual memory structure.