3 papers
math.OC2020
On the Convergence of Reinforcement Learning with Monte Carlo Exploring Starts
Jun Liu
A basic simulation-based reinforcement learning algorithm is the Monte Carlo Exploring States (MCES) method, also known as optimistic policy iteration, in which the value function…
math.OC2020
A Necessary Condition on Chain Reachable Robustness of Dynamical Systems
Maxwell Fitzsimmons, Jun Liu
It is ``folklore'' that the solution to a set reachability problem for a dynamical system is only noncomputable because of non-robustness reasons. A robustness condition that can b…
cs.AI2020
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks
Chuanzheng Wang, Yinan Li, Stephen L. Smith +1
In this paper, we propose a model-free reinforcement learning method to synthesize control policies for motion planning problems with continuous states and actions. The robot is mo…