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TOG
2002
112views more  TOG 2002»
15 years 3 months ago
Integrated learning for interactive synthetic characters
The ability to learn is a potentially compelling and important quality for interactive synthetic characters. To that end, we describe a practical approach to real-time learning fo...
Bruce Blumberg, Marc Downie, Yuri A. Ivanov, Matt ...
156
Voted

Publication
352views
15 years 11 months ago
Efficient methods for near-optimal sequential decision making under uncertainty
This chapter discusses decision making under uncertainty. More specifically, it offers an overview of efficient Bayesian and distribution-free algorithms for making near-optimal se...
Christos Dimitrakakis
118
Voted
AAMAS
2005
Springer
15 years 9 months ago
Learning to Coordinate Using Commitment Sequences in Cooperative Multi-agent Systems
We report on an investigation of the learning of coordination in cooperative multi-agent systems. Specifically, we study solutions that are applicable to independent agents i.e. ...
Spiros Kapetanakis, Daniel Kudenko, Malcolm J. A. ...
AAAI
2007
15 years 5 months ago
RETALIATE: Learning Winning Policies in First-Person Shooter Games
In this paper we present RETALIATE, an online reinforcement learning algorithm for developing winning policies in team firstperson shooter games. RETALIATE has three crucial chara...
Megan Smith, Stephen Lee-Urban, Hector Muño...
160
Voted
ML
2008
ACM
152views Machine Learning» more  ML 2008»
15 years 3 months ago
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path
Abstract. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems. As opposed to previous theoretical wo...
András Antos, Csaba Szepesvári, R&ea...