To address this issue, this paper proposes a novel risk- sensitive computational mechanism for biological motor control based on reinforcement learning (RL) and�...

Reinforcement-Learning-Based Risk-Sensitive Optimal Feedback Mechanisms of Biological Motor Control. December 2023. DOI:10.1109/CDC49753.2023.10384286.

Dive into the research topics of 'Reinforcement-Learning-Based Risk-Sensitive Optimal Feedback Mechanisms of Biological Motor Control'. Together they form a�...

This new theory is based on our recently developed adaptive dynamic programming (ADP) and robust ADP (RADP) methods and is especially useful for accounting for�...

... optimal stationary control of linear stochastic systems ... Reinforcement-Learning-Based Risk-Sensitive Optimal Feedback Mechanisms of Biological Motor Control.

Reinforcement-Learning-Based Risk-Sensitive Optimal Feedback Mechanisms of Biological Motor Control. Leilei Cui, Bo Pang, Zhong-Ping Jiang. 2023 IEEE 62nd�...

Reinforcement-Learning-Based Risk-Sensitive Optimal Feedback Mechanisms of Biological Motor Control. CDC 2023: 7944-7949. [c8]. view. electronic edition @ mlr�...

Dec 12, 2023 � This proposal aims to deepen our preliminary research in learning-based control theory as a new computational principle of sensorimotor control.

We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments.

A novel risk-sensitive computational mechanism for biological motor control based on reinforcement learning (RL) and adaptive dynamic programming (ADP) is�...