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» Metric learning for reinforcement learning agents
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89
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AAAI
2008
15 years 2 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
98
Voted
ATAL
2009
Springer
15 years 7 months ago
Bounded rationality via recursion
Current trends in model construction in the field of agentbased computational economics base behavior of agents on either game theoretic procedures (e.g. belief learning, fictit...
Maciej Latek, Robert L. Axtell, Bogumil Kaminski
IJSNET
2010
317views more  IJSNET 2010»
14 years 7 months ago
MRL-CC: a novel cooperative communication protocol for QoS provisioning in wireless sensor networks
: Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in terms of...
Xuedong Liang, Min Chen, Yang Xiao, Ilangko Balasi...
102
Voted
AAAI
1996
15 years 1 months ago
Dynamically Sequencing an Animated Pedagogical Agent
One of the most promising opportunities introduced by rapid advances in knowledge-based learning environments and multimedia technologies is the possibility of creating animated p...
Brian A. Stone, James C. Lester
115
Voted
ATAL
2008
Springer
15 years 2 months ago
Approximate predictive state representations
Predictive state representations (PSRs) are models that represent the state of a dynamical system as a set of predictions about future events. The existing work with PSRs focuses ...
Britton Wolfe, Michael R. James, Satinder P. Singh