%0 Journal Article %1 wang2016acer %A Wang, Ziyu %A Bapst, Victor %A Heess, Nicolas %A Mnih, Volodymyr %A Munos, Rémi %A Kavukcuoglu, Koray %A de Freitas, Nando %D 2016 %J CoRR %K DRLAlgoComparison acer reinforcement_learning %T Sample Efficient Actor-Critic with Experience Replay. %U http://dblp.uni-trier.de/db/journals/corr/corr1611.html#WangBHMMKF16 %V abs/1611.01224 %X 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.

@article{wang2016acer, 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. }, added-at = {2019-12-16T18:28:00.000+0100}, author = {Wang, Ziyu and Bapst, Victor and Heess, Nicolas and Mnih, Volodymyr and Munos, Rémi and Kavukcuoglu, Koray and de Freitas, Nando}, biburl = {https://www.bibsonomy.org/bibtex/2f6fb87ed0695c5aa692d83fc7cab7794/lanteunis}, ee = {http://arxiv.org/abs/1611.01224}, interhash = {ce284493dd67acc99e0c89e834a7161c}, intrahash = {f6fb87ed0695c5aa692d83fc7cab7794}, journal = {CoRR}, keywords = {DRLAlgoComparison acer reinforcement_learning}, timestamp = {2019-12-16T21:10:37.000+0100}, title = {Sample Efficient Actor-Critic with Experience Replay.}, url = {http://dblp.uni-trier.de/db/journals/corr/corr1611.html#WangBHMMKF16}, volume = {abs/1611.01224}, year = 2016 }