Model-Based Reinforcement Learning with System Identification and Fuzzy Reward

An approach for model-based reinforcement learning (MBRL) gets presented utilizing system identification and fuzzy reward formulation. Minimum order estimation gets applied first to determine the system order. This aids transfer function approximation of the linear time-invariant system. A Mamdani fuzzy inference mechanism defines the reward signal. The model obtained integrates into the Q-learning process to simulate experiences bypassing the environment. The proposed framework showcases sample-efficient learning without interacting with the true system. The applicability currently limits to linear systems. Extending the techniques to nonlinear, time-varying dynamics could increase applicability to real-world systems. This paper contributes an initial demonstration of MBRL using simple dynamics approximation and fuzzy rewards. Performance analysis provides evidence of learning improvement without explicitly communicating with the actual plant. A reduction in steady state error and settling time gets achieved.