Model predictive control-based value estimation for efficient reinforcement learning

arXiv:2310.16646v2 Announce Type: replace
Abstract: Reinforcement learning suffers from limitations in real practices primarily due to the number of required interactions with virtual environments. It results in a challenging problem because we are implausible to obtain a local optimal strategy with only a few attempts for many learning methods. Hereby, we design an improved reinforcement learning method based on model predictive control that models the environment through a data-driven approach. Based on the learned environment model, it performs multi-step prediction …

abstract arxiv control cs.lg environments interactions limitations practices predictive reinforcement reinforcement learning results strategy type value virtual virtual environments