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Linear least-squares algorithms for temporal difference learning
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Natural gradient works efficiently in learning
Neural Computation
Least-squares policy iteration
The Journal of Machine Learning Research
Tracking in Reinforcement Learning
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
An RLS-based natural actor-critic algorithm for locomotion of a two-linked robot arm
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Journal of Artificial Intelligence Research
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Actor-critics architectures have become popular during the last decade in the field of reinforcement learning because of the introduction of the policy gradient with function approximation theorem. It allows combining rationally actorcritic architectures with value function approximation and therefore addressing large-scale problems. Recent researches led to the replacement of policy gradient by a natural policy gradient, improving the efficiency of the corresponding algorithms. However, a common drawback of these approaches is that they require the manipulation of the so-called advantage function which does not satisfy any Bellman equation. Consequently, derivation of actor-critic algorithms is not straightforward. In this paper, we re-derive theorems in a way that allows reasoning directly with the state-action value function (or Q-function) and thus relying on the Bellman equation again. Consequently, new forms of critics can easily be integrated in the actor-critic framework.