Abstract
Though a reinforcement learning method is considered as a promising method for learning a robot's behavior from reward signals and adapting it for unknown environment, a standard reinforcement learning method is for a single environment. In this paper, to make a robot working in wider environments, we develop a reinforcement learning method for (1) estimating the current environment, (2) choosing a suitable policy for a known environment, and (3) making learning efficient when learning in a new environment by using transfer learning. To achieve them, we extend the learning strategy (LS) fusion method [1]. LS fusion is a method to learn multiple policies for a single task by applying multiple learning strategies (LSs) step by step. The key idea of environment estimation is using reward statistics of learned policies. For efficient learning, we design a learning strategy to transfer a policy learned in a different environment to one for the current environment. To verify the proposed method, we conducted some experiments where a small size humanoid robot learned a crawling task in several kinds of environments.
Original language | English |
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Pages | 605-612 |
Number of pages | 8 |
DOIs | |
Publication status | Published - 2013 Jan 1 |
Externally published | Yes |
Event | 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013 - Shenzhen, China Duration: 2013 Dec 12 → 2013 Dec 14 |
Other
Other | 2013 IEEE International Conference on Robotics and Biomimetics, ROBIO 2013 |
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Country/Territory | China |
City | Shenzhen |
Period | 13/12/12 → 13/12/14 |
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Biotechnology