SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY

paper available at https://arxiv.org/pdf/1611.01224.pdf

This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.

To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stochastic dueling network architectures, and a new trust region policy optimization method.