Experience selection in deep reinforcement learning for control

Experience selection in deep reinforcement learning for control

de Bruin, T.D. (TU Delft Learning & Autonomous Control)
Kober, J. (TU Delft Learning & Autonomous Control)
Tuyls, K.P. (Deepmind; University of Liverpool)
Babuska, R. (TU Delft Learning & Autonomous Control)

2018

Experience replay is a technique that allows off-policy reinforcement-learning methods to reuse past experiences. The stability and speed of convergence of reinforcement learning, as well as the eventual performance of the learned policy, are strongly dependent on the experiences being replayed. Which experiences are replayed depends on two important choices. The first is which and how many experiences to retain in the experience replay buffer. The second choice is how to sample the experiences that are to be replayed from that buffer. We propose new methods for the combined problem of experience retention and experience sampling. We refer to the combination as experience selection. We focus our investigation specifically on the control of physical systems, such as robots, where exploration is costly. To determine which experiences to keep and which to replay, we investigate different proxies for their immediate and long-term utility. These proxies include age, temporal difference error and the strength of the applied exploration noise. Since no currently available method works in all situations, we propose guidelines for using prior knowledge about the characteristics of the control problem at hand to choose the appropriate experience replay strategy.

Control
Deep learning
Experience replay
Reinforcement learning
Robotics

http://resolver.tudelft.nl/uuid:9daa8734-df0d-420a-ab11-040b3eb5e6a9

1532-4435

Journal of Machine Learning Research, 19 (9)

Institutional Repository

journal article

© 2018 T.D. de Bruin, J. Kober, K.P. Tuyls, R. Babuska