Paper Info
Title
Modeling the Opponent's Action Using Control-Based Reinforcement Learning.
Abstract
In this paper, we propose an alternative to model-free reinforcement learning approaches that recently have demonstrated Theory-of-Mind like behaviors. We propose a game theoretic approach to the problem in which pure RL has demonstrated to perform below the standards of human-human interaction. In this context, we propose alternative learning architectures that complement basic RL models with the ability to predict the other's actions. This architecture is tested in different scenarios where agents equipped with similar or varying capabilities compete in a social game. Our different interaction scenarios suggest that our model-based approaches are especially effective when competing against models of equivalent complexity, in contrast to our previous results with more basic predictive architectures. We conclude that the evolution of mechanisms that allow for the control of other agents provide different kinds of advantages that can become significant when interacting with different kinds of agents. We argue that no single proposed addition to the learning architecture is sufficient to optimize performance in these scenarios, but a combination of the different mechanisms suggested is required to achieve near-optimal performance in any case.
Year
DOI
Venue
2018
10.1007/978-3-319-95972-6_19
BIOMIMETIC AND BIOHYBRID SYSTEMS
Keywords
Field
DocType
Multi-agent models,Cognitive architectures,Theory of mind,Game theory,Social decision-making,Reinforcement learning
Architecture,Computer science,Theory of mind,Game theoretic,Game theory,Artificial intelligence,Adversary,Social decision making,Social games,Reinforcement learning
Conference
Volume
ISSN
Citations 
10928
0302-9743
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Ismael T. Freire100.34
Jordi-Ysard Puigbò200.68
Xerxes D. Arsiwalla38417.84
Paul F. M. J. Verschure4677116.64