Accelerated Reinforcement Learning for Temporal Logic Control Objectives
Yiannis Kantaros,Yiannis Kantaros
This paper addresses the problem of learning control policies for mobile robots, modeled as unknown Markov Decision Processes (MDPs), that are tasked with temporal logic missions, such as sequencing, coverage, or surveillance. The MDP captures uncertainty in the workspace structure and the outcomes of control decisions. The control objective is to synthesize a control policy that maximizes the pro...
