Adaptive Choice of Grid and Time in Reinforcement Learning
نویسنده
چکیده
We propose local error estimates together with algorithms for adap-tive a-posteriori grid and time reenement in reinforcement learning. We consider a deterministic system with continuous state and time with innnite horizon discounted cost functional. For grid re-nement we follow the procedure of numerical methods for the Bellman-equation. For time reenement we propose a new criterion, based on consistency estimates of discrete solutions of the Bellman-equation. We demonstrate, that an optimal ratio of time to space discretization is crucial for optimal learning rates and accuracy of the approximate optimal value function.
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