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ML
1998
ACM

Elevator Group Control Using Multiple Reinforcement Learning Agents

11 years 11 months ago
Elevator Group Control Using Multiple Reinforcement Learning Agents
Recent algorithmic and theoretical advances in reinforcement learning (RL) have attracted widespread interest. RL algorithmshave appeared that approximatedynamic programming on an incremental basis. They can be trained on the basis of real or simulated experiences, focusing their computation on areas of state space that are actually visited during control, making them computationally tractable on very large problems. If each member of a team of agents employs one of these algorithms, a new collective learning algorithm emerges for the team as a whole. In this paper we demonstratethat such collective RL algorithms can be powerful heuristic methods for addressing large{scale control problems. Elevator group control serves as our testbed. It is a di cult domain posing a combination of challengesnotseenin mostmulti-agentlearningresearchto date. We usea teamof RL agents,each of which is responsible for controlling one elevator car. The team receives a global reinforcement signal which appea...
Robert H. Crites, Andrew G. Barto
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1998
Where ML
Authors Robert H. Crites, Andrew G. Barto
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