Multiagent learning using a variable learning rate
Artificial Intelligence
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Modeling non-stationary opponents
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Interactions among agents are complicated since in order to make the best decisions, each agent has to take into account not only the strategy used by other agents but also how those strategies might change in the future (and what causes these changes). The objective of my work will be to develop a framework for learning agent models (opponent or teammate) more accurately and with less interactions, with a special focus on fast learning non-stationary strategies. As preliminary work we have proposed an initial approach for learning nonstationary strategies in repeated games. We use decision trees to learn a model of the agent, and we transform the learned trees into a MDP and solve it to obtain the optimal policy.