Time‐in‐action RL. Issue 1 (4th October 2019)

• Record Type: Journal Article
• Authors: Zhu, Jiangcheng; Wang, Zhepei; Mcilwraith, Douglas; Wu, Chao; Xu, Chao; Guo, Yike
• Is Part Of: IET cyber-systems and robotics. Volume 1:Issue 1(2019)
• Page Start: 28
• Page End: 37
• Publication Date: 2019-10-04
• Subjects: learning (artificial intelligence) -- reinforcement learning framework -- control theoretical formalism -- explicit time function -- action value -- action time -- RL value network -- embedded time model -- time‐in‐action RL; Robotics -- Periodicals; Cybernetics -- Periodicals; Cybernetics; Robotics; Periodicals; 629
• DOI: 10.1049/iet-csr.2018.0001
• Languages: English
• ISSNs: 2631-6315

Abstract

Abstract : The authors propose a novel reinforcement learning (RL) framework, where agent behaviour is governed by traditional control theory. This integrated approach, called time‐in‐action RL, enables RL to be applicable to many real‐world systems, where underlying dynamics are known in their control theoretical formalism. The key insight to facilitate this integration is to model the explicit time function, mapping the state‐action pair to the time accomplishing the action by its underlying controller. In their framework, they describe an action by its value (action value), and the time that it takes to perform (action time). An action‐value results from the policy of RL regarding a state. Action time is estimated by an explicit time model learnt from the measured activities of the underlying controller. RL value network is then trained with embedded time model to predict action time. This approach is tested using a variant of Atari Pong and proved to be convergent.