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NIPS
1992
15 years 24 days ago
Explanation-Based Neural Network Learning for Robot Control
How can artificial neural nets generalize better from fewer examples? In order to generalize successfully, neural network learning methods typically require large training data se...
Tom M. Mitchell, Sebastian Thrun
CORR
2010
Springer
152views Education» more  CORR 2010»
14 years 11 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
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...
107
Voted
ENTER
2009
Springer
15 years 6 months ago
Learning Adaptive Recommendation Strategies for Online Travel Planning
Conversational recommender systems support human-computer interaction strategies in order to assist online tourists in the important activity of dynamic packaging, i.e., in buildi...
Tariq Mahmood, Francesco Ricci, Adriano Venturini
ICMLA
2008
15 years 1 months ago
A Predictive Model for Imitation Learning in Partially Observable Environments
Learning by imitation has shown to be a powerful paradigm for automated learning in autonomous robots. This paper presents a general framework of learning by imitation for stochas...
Abdeslam Boularias