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Conference Papers Year : 2016

Off-Policy Neural Fitted Actor-Critic

Yann Boniface
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Alain Dutech


A new off-policy, offline, model-free, actor-critic reinforcement learning algorithm dealing with continuous environments in both states and actions is presented. It addresses discrete time problems where the goal is to maximize the discounted sum of rewards using stationary policies. Our algorithm allows to trade-off between data-efficiency and scalability. The amount of a priori knowledge is kept low by: (1) using neural networks to learn both the critic and the actor, (2) not relying on initial trajectories provided by an expert, and (3) not depending on known goal states. Experimental results compare data-efficiency to 4 state-of-the-art algorithms on three benchmark environments. This article largely reproduces a previous work [34] by adding a higher dimensional environment, improving control architectures and provides batch normalization for others state-of-the-art algorithms.
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hal-01413886 , version 1 (11-12-2016)


  • HAL Id : hal-01413886 , version 1


Matthieu Zimmer, Yann Boniface, Alain Dutech. Off-Policy Neural Fitted Actor-Critic. NIPS 2016 - Deep Reinforcement Learning Workshop, Dec 2016, Barcelona, Spain. ⟨hal-01413886⟩
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