Subgoal Identification for Reinforcement Learning and Planning in Multiagent Problem Solving
We provide a new probability flow analysis algorithm to automatically identify subgoals in a problem space. Our flow analysis, inspired by preflow-push algorithms, measures the topological structure of the problem space to identify states that connect different subset of state space as the subgoals within linear-time complexity. Then we apply a hybrid approach known as subgoal-based SMDP (semi-Markov Decision Process) that is composed of reinforcement learning and planning based on the identified subgoals to solve the problem in a multiagent environment. The effectiveness of this new method used in a multiagent system is demonstrated and evaluated using a capture-the-flag scenario. We showed also that the cooperative coordination emerged between two agents in the scenario through distributed policy learning.
- Subgoal Identification for Reinforcement Learning and Planning in Multiagent Problem Solving
- Book Title
- Multiagent System Technologies
- Book Subtitle
- 5th German Conference, MATES 2007, Leipzig, Germany, September 24-26, 2007. Proceedings
- pp 37-48
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
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- Author Affiliations
- 1. Department of Computer Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu, Taiwan, R.O.C.
- 2. Department of Computer Science and Information Engineering, National Kaohsiung University, 700, Kaohsiung University Rd, Nan Tzu Dist., 811. Kaohsiung, Taiwan, R.O.C.
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