Noisy Importance Sampling Actor-Critic: An Off-Policy Actor-Critic With Experience Replay

摘要

This paper presents Noisy Importance Sampling Actor-Critic (NISAC), a set of empirically validated modifications to the advantage actor-critic algorithm (A2C), allowing off-policy reinforcement learning and increased performance. NISAC uses additive action space noise, aggressive truncation of importance sample weights, and large batchsizes. We see that additive noise drastically changes how off-sample experience is weighted for policy updates. The modified algorithm achieves an increase in convergence speed and sample efficiency compared to both the on-policy actor-critic A2C and the importance weighted off-policy actor-critic algorithm. In comparison to state-of-the-art (SOTA) methods, such as actor-critic with experience replay (ACER), NISAC nears the performance on several of the tested environments while training 40% faster and being significantly easier to implement. The effectiveness of NISAC is demonstrated against existing on-policy and off-policy actor-critic algorithms on a subset of the Atari domain.