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Sample Efficient Actor-Critic with Experience Replay

Authors
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, Nando de Freitas
Year
2016
Preprint
Algorithm
ACER
Source
arXiv
Identifiers
arXiv 1611.01224

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.

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Tags

Reinforcement learning Optimization Control Learning Sampling

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