02/02/2021

Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation

Bo Pang, Zhong-Ping Jiang

Keywords:

Abstract Paper Similar Papers

Abstract: This paper studies the robustness of reinforcement learning algorithms to errors in the learning process. Specifically, we revisit the benchmark problem of discrete-time linear quadratic regulation (LQR) and study the long-standing open question: Under what conditions is the policy iteration method robustly stable from a dynamical systems perspective? Using advanced stability results in control theory, it is shown that policy iteration for LQR is inherently robust to small errors in the learning process and enjoys small-disturbance input-to-state stability: whenever the error in each iteration is bounded and small, the solutions of the policy iteration algorithm are also bounded, and, moreover, enter and stay in a small neighborhood of the optimal LQR solution. As an application, a novel off-policy optimistic least-squares policy iteration for the LQR problem is proposed, when the system dynamics are subjected to additive stochastic disturbances. The proposed new results in robust reinforcement learning are validated by a numerical example.

The video of this talk cannot be embedded. You can watch it here:
https://slideslive.com/38947791
(Link will open in new window)
 0
 0
 0
 0
  Share    
This is an embedded video. Talk and the respective paper are published at AAAI 2021 virtual conference. If you are one of the authors of the paper and want to manage your upload, see the question "My papertalk has been externally embedded..." in the FAQ section.

Comments

Post Comment
no comments yet
code of conduct: tbd Characters remaining: 140

Similar Papers

 0
 0
 0
 0
 3:11 
 0
 0
 0
 0
 13:11 
 0
 0
 0
 0
 14:22 
 0
 0
 0
 0
 14:00 
 0
 0
 0
 0
 3:02 
 0
 0
 0
 0
 5:43 
 0
 0
 0
 0
 3:05 
 0
 0
 0
 0
 5:15 
 0
 0
 0
 0
 13:28 
 0
 0
 0
 0
 2:44 
 0
 0
 0
 0
 3:25 
 0
 0
 0
 0
 3:20 
 0
 0
 0
 0
 10:21 
 0
 0
 0
 0
 5:35 
 0
 0
 0
 0
 14:45 
 1
 0
 0
 0
 13:50 
 0
 0
 0
 0
 12:35 
 0
 0
 0
 0
 9:02 
 0
 0
 0
 0
 17:07 
 0
 0
 0
 0
 5:06 
 0
 0
 0
 0
 3:11 
 0
 0
 0
 0
 17:58 
 0
 0
 0
 0
 5:03 
 0
 0
 0
 0
 3:24 
 0
 0
 0
 0
 15:20 
 0
 0
 0
 0
 14:10 
 0
 0
 0
 0
 12:55 
 0
 0
 0
 0
 13:36 
 0
 0
 0
 0
 4:05 
 0
 0
 0
 0
 12:47 
 1
 0
 0
 0
 13:54 
 0
 0
 0
 0
 8:47 
 0
 0
 0
 0
 11:59 
 0
 0
 0
 0
 13:26 
 0
 0
 0
 0
 15:18 
 0
 0
 0
 0
 4:29 
 0
 0
 0
 0
 4:40 
 0
 0
 0
 0
 3:09 
 0
 0
 0
 0
 5:16 
 0
 0
 0
 0
 14:38