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Totally model-free reinforcement learning by actor-critic Elman networks in non-Markovian domains

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1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227)
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In this paper we describe how a n actor-critic reinforcement learning agent in a non-Markovian domain nds an optimal sequence of actions in a totally modelfree fashion that is, the agent neither learns transitional probabilities and associated rewards, nor by h o w m uch the state space should be augmented so that the Markov p r o perty holds. In particular, we employ an Elman-type recurrent neural network to solve non-Markovian problems since an Elman-type network is able to implicitly and

doi:10.1109/ijcnn.1998.687169
fatcat:r44xdijcynf7jftn6kqpp362nq