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Neural Fitted Actor-Critic

Matthieu Zimmer
Yann Boniface
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  • PersonId : 778629
  • IdRef : 140410805
Alain Dutech


A novel reinforcement learning algorithm that deals with both continuous state and action spaces is proposed. Domain knowledge requirements are kept minimal by using non-linear estimators and since the algorithm does not need prior trajectories or known goal states. The new actor-critic algorithm is on-policy, offline and model-free. It considers discrete time, stationary policies, and maximizes the discounted sum of rewards. Experimental results on two common environments, showing the good performance of the proposed algorithm, are presented.
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hal-01350651 , version 1 (23-08-2016)


  • HAL Id : hal-01350651 , version 1


Matthieu Zimmer, Yann Boniface, Alain Dutech. Neural Fitted Actor-Critic. ESANN 2016 - Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2016, Bruges, Belgium. ⟨hal-01350651⟩
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