Abstract
This paper presents a policy gradient multi-agent reinforcement learning algorithm for leader-follower systems. In this algorithm, cooperative dynamics of the leader-follower control is modelled as an incentive Stackelberg game. A linear incentive mechanism is used to connect the leader and follower policies. Policy gradient reinforcement learning explicitly explores policy parameter space to search the optimal policy. Fuzzy logic controllers are used as the policy. The parameters of fuzzy logic controllers can be improved by this policy gradient algorithm.
| Original language | English |
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| Title of host publication | 2005 IEEE International Conference on Mechatronics & Automations |
| Subtitle of host publication | Conference Proceedings |
| Editors | Jason Gu, Peter X. Liu |
| Place of Publication | Piscataway, NJ. |
| Publisher | IEEE |
| Pages | 1557-1561 |
| Number of pages | 5 |
| Volume | 3 |
| ISBN (Print) | 078039044X |
| DOIs | |
| Publication status | Published - 1 Jul 2005 |
| Event | IEEE International Conference on Mechatronics and Automation, ICMA 2005 - Niagara Falls, ON, United Kingdom Duration: 29 Jul 2005 → 1 Aug 2005 |
Conference
| Conference | IEEE International Conference on Mechatronics and Automation, ICMA 2005 |
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| Country/Territory | United Kingdom |
| City | Niagara Falls, ON |
| Period | 29/07/05 → 1/08/05 |
Keywords
- incentive Stackelberg game
- multi-agent reinforcement learning
- policy gradient reinforcement learning
- control engineering computing
- fuzzy logic
- game theory
- learning (artificial intelligence)
- multi-agent systems