Autonomous reinforcement learning with experience replay
Paweł Wawrzyński , Ajay Kumar Tanwani
AbstractThis paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor–critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time.
|Journal series||Neural Networks, ISSN 0893-6080|
|Keywords in English||Actor–critic, Autonomous learning, Reinforcement learning, Step-size estimation|
|Score|| = 30.0, 01-09-2020, ArticleFromJournal|
= 30.0, 01-09-2020, ArticleFromJournal
|Publication indicators||= 16; = 25; = 49.0; : 2013 = 2.008; : 2013 = 2.076 (2) - 2013=2.516 (5)|
|Citation count*||49 (2020-09-23)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.