markov decision process

Variational methods for Reinforcement Learning AISTATS 2010 Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation NeurIPS 2009 Help or Hinder: Bayesian Models of Social Goal Inference NeurIPS 2009 Reinforcement Learning in Finite MDPs: PAC Analysis JMLR 2009 A Convergent $O(n)$ Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation NeurIPS 2008 Bayesian Model of Behaviour in Economic Games NeurIPS 2008 Biasing Approximate Dynamic Programming with a Lower Discount Factor NeurIPS 2008 Bounding Performance Loss in Approximate MDP Homomorphisms NeurIPS 2008 Finite-Time Bounds for Fitted Value Iteration JMLR 2008 Goal-directed decision making in prefrontal cortex: a computational framework NeurIPS 2008 MDPs with Non-Deterministic Policies NeurIPS 2008 Near-optimal Regret Bounds for Reinforcement Learning NeurIPS 2008 Value Function Based Reinforcement Learning in Changing Markovian Environments JMLR 2008 Fitted Q-iteration in continuous action-space MDPs NeurIPS 2007 Online Linear Regression and Its Application to Model-Based Reinforcement Learning NeurIPS 2007 Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs NeurIPS 2007 Proto-value Functions: A Laplacian Framework for Learning Representation and Control in Markov Decision Processes JMLR 2007 Scan Strategies for Meteorological Radars NeurIPS 2007 The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty RSS 2007 Linearly-solvable Markov decision problems NeurIPS 2006 Sample Complexity of Policy Search with Known Dynamics NeurIPS 2006 The Robustness-Performance Tradeoff in Markov Decision Processes NeurIPS 2006 An MDP-Based Recommender System JMLR 2005 On the Convergence of Optimistic Policy Iteration JMLR 2002 ε-MDPs: Learning in Varying Environments JMLR 2002