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high-dimensional control

topic1 events
papersTODAY 04:00 UTC

Paper studies reinforcement learning for stochastic control with unknown drift and rewards

A new arXiv preprint examines continuous-time stochastic control problems where both the drift coefficients and the running reward functions are not known in advance. The authors adopt an exploratory reinforcement learning approach to handle these missing model components, and they provide theoretical analysis along with algorithms and convergence results. The work targets settings that may be high-dimensional.

reinforcement-learning/research/stochastic-control/arxiv