Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning
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Published By Springer Berlin Heidelberg

9783642299452, 9783642299469

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Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_7 ◽

2012 ◽

pp. 30-41

Author(s):

Matthew Robards ◽

Peter Sunehag

Keyword(s):

Loss Functions ◽

Policy Control ◽

Gradient Based

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An Extension of a Hierarchical Reinforcement Learning Algorithm for Multiagent Settings

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_26 ◽

2012 ◽

pp. 261-272

Author(s):

Ioannis Lambrou ◽

Vassilis Vassiliades ◽

Chris Christodoulou

Keyword(s):

Reinforcement Learning ◽

Learning Algorithm ◽

Hierarchical Reinforcement Learning ◽

Reinforcement Learning Algorithm

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Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement Learning

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_2 ◽

2012 ◽

pp. 2-2

Author(s):

Kristian Kersting

Keyword(s):

Reinforcement Learning ◽

Representational Power

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Invited Talk: UCRL and Autonomous Exploration

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_1 ◽

2012 ◽

pp. 1-1

Author(s):

Peter Auer

Keyword(s):

Autonomous Exploration

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Bayesian Multitask Inverse Reinforcement Learning

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_27 ◽

2012 ◽

pp. 273-284 ◽

Cited By ~ 15

Author(s):

Christos Dimitrakakis ◽

Constantin A. Rothkopf

Keyword(s):

Reinforcement Learning ◽

Inverse Reinforcement Learning

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MapReduce for Parallel Reinforcement Learning

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_30 ◽

2012 ◽

pp. 309-320 ◽

Cited By ~ 5

Author(s):

Yuxi Li ◽

Dale Schuurmans

Keyword(s):

Reinforcement Learning

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Multi-Task Reinforcement Learning: Shaping and Feature Selection

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_24 ◽

2012 ◽

pp. 237-248 ◽

Cited By ~ 3

Author(s):

Matthijs Snel ◽

Shimon Whiteson

Keyword(s):

Feature Selection ◽

Reinforcement Learning

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Robust Bayesian Reinforcement Learning through Tight Lower Bounds

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_19 ◽

2012 ◽

pp. 177-188 ◽

Cited By ~ 1

Author(s):

Christos Dimitrakakis

Keyword(s):

Reinforcement Learning ◽

Lower Bounds ◽

Bayesian Reinforcement Learning

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Value Function Approximation through Sparse Bayesian Modeling

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_15 ◽

2012 ◽

pp. 128-139 ◽

Cited By ~ 3

Author(s):

Nikolaos Tziortziotis ◽

Konstantinos Blekas

Keyword(s):

Bayesian Modeling ◽

Function Approximation ◽

Value Function ◽

Value Function Approximation

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Unified Inter and Intra Options Learning Using Policy Gradient Methods

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning ◽

10.1007/978-3-642-29946-9_17 ◽

2012 ◽

pp. 153-164 ◽

Cited By ~ 3

Author(s):

Kfir Y. Levy ◽

Nahum Shimkin

Keyword(s):

Gradient Methods ◽

Policy Gradient

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Lecture Notes in Computer Science - Recent Advances in Reinforcement LearningLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer Berlin Heidelberg

Gradient Based Algorithms with Loss Functions and Kernels for Improved On-Policy Control

An Extension of a Hierarchical Reinforcement Learning Algorithm for Multiagent Settings

Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement Learning

Invited Talk: UCRL and Autonomous Exploration

Bayesian Multitask Inverse Reinforcement Learning

MapReduce for Parallel Reinforcement Learning

Multi-Task Reinforcement Learning: Shaping and Feature Selection

Robust Bayesian Reinforcement Learning through Tight Lower Bounds

Value Function Approximation through Sparse Bayesian Modeling

Unified Inter and Intra Options Learning Using Policy Gradient Methods

Lecture Notes in Computer Science - Recent Advances in Reinforcement Learning
Latest Publications