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ATAL
2015
Springer

Opponent Modeling against Non-stationary Strategies: (Doctoral Consortium)

8 years 7 months ago
Opponent Modeling against Non-stationary Strategies: (Doctoral Consortium)
Most state of the art learning algorithms do not fare well with agents (computer or humans) that change their behaviour in time. This is the case because they usually do not model the other agents’ behaviour and instead make some assumptions that for real scenarios are too restrictive. Furthermore, considering that many applications demand different types of agents to work together this should be an important problem to solve. We contribute to the state of the art with opponent modeling algorithms. In particular we proposed 3 approaches for learning against non-stationary opponents in repeated games. Experimentally we tested our approaches on three domains including a real world scenario which consists of bidding in energy markets. Keywords Non-stationary strategies; opponent modelling; Markov decision process; learning
Pablo Hernandez-Leal, Enrique Munoz de Cote, Luis
Added 16 Apr 2016
Updated 16 Apr 2016
Type Journal
Year 2015
Where ATAL
Authors Pablo Hernandez-Leal, Enrique Munoz de Cote, Luis Enrique Sucar
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