Abstract
A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for dealing with the learning and control issues in cooperative multiagent systems with continuous states and actions, particularly for autonomous robotic formation systems. The parameters of fuzzy policy are finely tuned by the gradient multiagent reinforcement learning algorithm to improve the overall performance of an initial controller (policy). A leader-follower robotic system is chosen as a platform to benchmark the performance of the multiagent fuzzy policy reinforcement learning algorithm. Our simulation results demonstrate that the control performance can be improved in many aspects. This work also can be seen as a scaling up of currently popular multiagent reinforcement learning to the robotic domain with continuous state and action space as well as high dimensionality.
| Original language | English |
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| Title of host publication | 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems |
| Place of Publication | Piscataway, NJ. |
| Publisher | IEEE |
| Pages | 3197-3202 |
| Number of pages | 6 |
| ISBN (Print) | 1424402581 |
| DOIs | |
| Publication status | Published - 2006 |
| Event | 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 - Beijing, United Kingdom Duration: 9 Oct 2006 → 15 Oct 2006 |
Conference
| Conference | 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2006 |
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| Country/Territory | United Kingdom |
| City | Beijing |
| Period | 9/10/06 → 15/10/06 |
Keywords
- cooperative control
- fuzzy reinforcement learning
- leader-follower robotic systems
- policy gradient reinforcement learning
- fuzzy control
- gradient methods
- learning (artificial intelligence)
- multi-agent systems
- multi-robot systems
- position control