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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/3851| Title: | All learning is local: Multi-agent learning in global reward games |
| Keywords: | Kalman filtering multi-agent systems Q-learning reinforcement learning |
| Issue Date: | 9-Oct-2013 |
| Description: | In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and effectively learn a near-optimal policy in a wide variety of settings. A sequence of increasingly complex empirical tests verifies the efficacy of this technique. Singapore-MIT Alliance (SMA) |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | http://hdl.handle.net/1721.1/3851 |
| Appears in Collections: | MIT Items |
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