10 search results - page 2 / 2 » Near-optimal Regret Bounds for Reinforcement Learning |
ICML
2006
IEEE
14 years 5 months ago
2006
IEEE
For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm--Delayed Q-Learning. We prove it is PAC, achieving near o...
ML
2002
ACM
13 years 4 months ago
2002
ACM
Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while t...
ATAL
2006
Springer
13 years 8 months ago
2006
Springer
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
JMLR
2012
11 years 7 months ago
2012
Contextual bandit learning is a reinforcement learning problem where the learner repeatedly receives a set of features (context), takes an action and receives a reward based on th...
CIMCA
2008
IEEE
13 years 11 months ago
2008
IEEE
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...