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AIPS
2008
13 years 10 months ago
Learning Heuristic Functions through Approximate Linear Programming
Planning problems are often formulated as heuristic search. The choice of the heuristic function plays a significant role in the performance of planning systems, but a good heuris...
Marek Petrik, Shlomo Zilberstein
ICRA
2009
IEEE
259views Robotics» more  ICRA 2009»
14 years 2 months ago
Constructing action set from basis functions for reinforcement learning of robot control
Abstract— Continuous action sets are used in many reinforcement learning (RL) applications in robot control since the control input is continuous. However, discrete action sets a...
Akihiko Yamaguchi, Jun Takamatsu, Tsukasa Ogasawar...
ICML
2005
IEEE
14 years 8 months ago
Proto-value functions: developmental reinforcement learning
This paper presents a novel framework called proto-reinforcement learning (PRL), based on a mathematical model of a proto-value function: these are task-independent basis function...
Sridhar Mahadevan
PKDD
2009
Springer
144views Data Mining» more  PKDD 2009»
14 years 2 months ago
Compositional Models for Reinforcement Learning
Abstract. Innovations such as optimistic exploration, function approximation, and hierarchical decomposition have helped scale reinforcement learning to more complex environments, ...
Nicholas K. Jong, Peter Stone
AAAI
2008
13 years 10 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...