Year: 2009
Type: conference-paper
Abstract: —A method for using the Time Hopping technique as a tool for probability redistribution is proposed. Applied to reinforcement learning in a simulation, it is able to re-shape the state probability distribution of the underlying Markov decision process as desired. This is achieved by modifying the target selection strategy of Time Hopping appropriately. Experiments with a robot maze reinforcement learning problem show that the method improves the exploration efficiency by re-shaping the state probability distribution to an almost uniform distribution.
Source: Spiral (Imperial College London)
Authors: Petar Kormushev, Fangyan Dong, Kaoru Hirota
Institution: Tokyo Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2402677255