title: Sample Efficient Actor-Critic with  Experience Replay
abstract: 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.
authors: Ziyu Wang Victor Bapst Nicolas Heess Volodymyr Mnih Remi Munos Koray Kavukcuoglu Nando de Freitas
hIndex: Remi Munos 72