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**Autonomous reinforcement learning with experience replay.**
*(English)*
Zbl 1296.68151

Summary: This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time.

### MSC:

68T05 | Learning and adaptive systems in artificial intelligence |

68T40 | Artificial intelligence for robotics |

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\textit{P. Wawrzyński} and \textit{A. K. Tanwani}, Neural Netw. 41, 156--167 (2013; Zbl 1296.68151)

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