Q-Learning with Double Progressive Widening : Application to Robotics
Abstract
Discretization of state and action spaces is a critical issue in $Q$-Learning. In our contribution, we propose a real-time adaptation of the discretization by the progressive widening technique which has been already used in bandit-based methods. Results are consistently converging to the optimum of the problem, without changing the parametrization for each new problem.
Domains
Machine Learning [cs.LG]Origin | Files produced by the author(s) |
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