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ECAI
2006
Springer
14 years 17 days ago
Learning by Automatic Option Discovery from Conditionally Terminating Sequences
Abstract. This paper proposes a novel approach to discover options in the form of conditionally terminating sequences, and shows how they can be integrated into reinforcement learn...
Sertan Girgin, Faruk Polat, Reda Alhajj
ICML
2005
IEEE
14 years 9 months ago
High speed obstacle avoidance using monocular vision and reinforcement learning
We consider the task of driving a remote control car at high speeds through unstructured outdoor environments. We present an approach in which supervised learning is first used to...
Jeff Michels, Ashutosh Saxena, Andrew Y. Ng
IJCNN
2008
IEEE
14 years 3 months ago
Self-organizing neural models integrating rules and reinforcement learning
— Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural m...
Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
14 years 16 days ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone
ICANN
2007
Springer
14 years 3 months ago
Solving Deep Memory POMDPs with Recurrent Policy Gradients
Abstract. This paper presents Recurrent Policy Gradients, a modelfree reinforcement learning (RL) method creating limited-memory stochastic policies for partially observable Markov...
Daan Wierstra, Alexander Förster, Jan Peters,...