Sample Efficient Actor-Critic with Experience Replay

Ziyu Wang; Victor Bapst; Nicolas Heess; Volodymyr Mnih; Remi Munos; Koray Kavukcuoglu; Nando de Freitas

ICLR 2017poster1079 citations

Sample Efficient Actor-Critic with Experience Replay

Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, Nando de Freitas

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.

Deep learningReinforcement Learning

BibTeX

@inproceedings{
wang2017sample,
title={Sample Efficient Actor-Critic with  Experience Replay},
author={Ziyu Wang and Victor Bapst and Nicolas Heess and Volodymyr Mnih and Remi Munos and Koray Kavukcuoglu and Nando de Freitas},
booktitle={International Conference on Learning Representations},
year={2017},
url={https://openreview.net/forum?id=HyM25Mqel}
}