Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning

no code implementations • 11 Sep 2022 • YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng

Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a strong need for automatic tuning methods.

Autonomous Driving reinforcement-learning +1

Zeroth-Order Actor-Critic

no code implementations • 29 Jan 2022 • YuHeng Lei, Jianyu Chen, Shengbo Eben Li, Sifa Zheng

The recent advanced evolution-based zeroth-order optimization methods and the policy gradient-based first-order methods are two promising alternatives to solve reinforcement learning (RL) problems with complementary advantages.

Continuous Control Reinforcement Learning (RL)