  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.
