Free-energy based reinforcement learning was proposed for learning in high-dimensional state and action spaces, which cannot be handled by standard function approximation methods in reinforcement learning. In the free-energy reinforcement learning method, the action-value function is approximated as the negative free energy of a restricted Boltzmann machine. In this paper, we test if it is feasible to use free-energy reinforcement learning for real robot control with raw, high-dimensional sensory inputs through the extraction of task-relevant features in the hidden layer. We first demonstrate, in simulation, that a small mobile robot could efficiently learn a vision-based navigation and battery capturing task. We then demonstrate, for a simpler battery capturing task, that free-energy reinforcement learning can be used for on-line learning in a real robot. The analysis of learned weights showed that action-oriented state coding was achieved in the hidden layer. © 2010 Springer-Verlag.
CITATION STYLE
Elfwing, S., Otsuka, M., Uchibe, E., & Doya, K. (2010). Free-energy based reinforcement learning for vision-based navigation with high-dimensional sensory inputs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6443 LNCS, pp. 215–222). https://doi.org/10.1007/978-3-642-17537-4_27
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