Tsallis Entropy Regularization for Linearly Solvable MDP and Linear Quadratic Regulator

arXiv:2403.01805v1 Announce Type: cross
Abstract: Shannon entropy regularization is widely adopted in optimal control due to its ability to promote exploration and enhance robustness, e.g., maximum entropy reinforcement learning known as Soft Actor-Critic. In this paper, Tsallis entropy, which is a one-parameter extension of Shannon entropy, is used for the regularization of linearly solvable MDP and linear quadratic regulators. We derive the solution for these problems and demonstrate its usefulness in balancing between exploration and sparsity of the obtained control …

abstract actor actor-critic arxiv control cs.lg cs.sy eess.sy entropy exploration extension linear math.oc paper promote regularization regulator reinforcement reinforcement learning robustness type