Multi-Agent Informational Learning Processes
- URL: http://arxiv.org/abs/2006.06870v4
- Date: Thu, 25 Feb 2021 21:43:47 GMT
- Title: Multi-Agent Informational Learning Processes
- Authors: J.K. Terry, Nathaniel Grammel
- Abstract summary: We introduce a new mathematical model of multi-agent reinforcement learning, the Multi-Agent Informational Learning Processor "MAILP" model.
The model is based on the notion that agents have policies for a certain amount of information, models how this information iteratively evolves and propagates through many agents.
- Score: 0.571097144710995
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce a new mathematical model of multi-agent reinforcement learning,
the Multi-Agent Informational Learning Processor "MAILP" model. The model is
based on the notion that agents have policies for a certain amount of
information, models how this information iteratively evolves and propagates
through many agents. This model is very general, and the only meaningful
assumption made is that learning for individual agents progressively slows over
time.
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