Neural Reinforcement Learning for an Obstacle Avoidance Behavior
نویسنده
چکیده
Reinforcement learning (RL) offers a set of various algorithms for in-situation behavior synthesis [1]. The Qlearning [2] technique is certainly the most used of the RL methods. Multilayer perceptron implementations of the Q-learning have been proposed early [3], due to the interest of the restricted memory need and the generalization capability [4]. Self-organizing map implementation of the Q-learning is more recent [5]. We propose to study the use and discuss the interest of this implementation comparing to a multilayer perceptron implementation or more classical ones. Experiments are performed in the real world with the miniature robot Khepera [6].
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