A Novel Adaptive Sampling Strategy for Deep Reinforcement Learning

Reinforcement learning, as an effective method to solve complex sequential decision-making problems, plays an important role in areas such as intelligent decision-making and behavioral cognition. It is well known that the sample experience replay mechanism contributes to the development of current deep reinforcement learning by reusing past samples to improve the efficiency of samples. However, the existing priority experience replay mechanism changes the sample distribution in the sample set due to the higher sampling frequency assigned to a specific transition, and it cannot be applied to actor-critic and other on-policy reinforcement learning algorithm. To address this, we propose an adaptive factor based on TD-error, which further increases sample utilization by giving more attention weight to samples of larger TD-error, and embeds it flexibly into the original Deep Q Network and Advantage Actor-Critic algorithm to improve their performance. Then we carried out the performance evaluation for the proposed architecture in the context of CartPole-V1 and 6 environments of Atari game experiments, respectively, and the obtained results either on the conditions of fixed temperature or annealing temperature, when compared to those produced by the vanilla DQN and original A2C, highlight the advantages in cumulative rewards and climb speed of the improved algorithms.