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ATAL
2008
Springer
13 years 9 months ago
Transfer of task representation in reinforcement learning using policy-based proto-value functions
Reinforcement Learning research is traditionally devoted to solve single-task problems. Therefore, anytime a new task is faced, learning must be restarted from scratch. Recently, ...
Eliseo Ferrante, Alessandro Lazaric, Marcello Rest...
NIPS
1998
13 years 9 months ago
Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
Michael J. Kearns, Satinder P. Singh
CSL
2012
Springer
12 years 3 months ago
Reinforcement learning for parameter estimation in statistical spoken dialogue systems
Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estim...
Filip Jurcícek, Blaise Thomson, Steve Young
IJCAI
2007
13 years 9 months ago
Bayesian Inverse Reinforcement Learning
Inverse Reinforcement Learning (IRL) is the problem of learning the reward function underlying a Markov Decision Process given the dynamics of the system and the behaviour of an e...
Deepak Ramachandran, Eyal Amir
CIA
2007
Springer
14 years 1 months ago
Multi-agent Learning Dynamics: A Survey
Abstract. In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration a...
H. Jaap van den Herik, Daniel Hennes, Michael Kais...