Policy Learning

Integrating a Partial Model into Model Free Reinforcement Learning JMLR 2012 Inverse Reinforcement Learning through Structured Classification NeurIPS 2012 Learning Partially Observable Models Using Temporally Abstract Decision Trees NeurIPS 2012 Neurally Plausible Reinforcement Learning of Working Memory Tasks NeurIPS 2012 Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions NeurIPS 2012 Online Regret Bounds for Undiscounted Continuous Reinforcement Learning NeurIPS 2012 On-line Reinforcement Learning Using Incremental Kernel-Based Stochastic Factorization NeurIPS 2012 On Stochastic Optimal Control and Reinforcement Learning by Approximate Inference RSS 2012 On the Use of Non-Stationary Policies for Stationary Infinite-Horizon Markov Decision Processes NeurIPS 2012 Risk Aversion in Markov Decision Processes via Near Optimal Chernoff Bounds NeurIPS 2012 Robustness and risk-sensitivity in Markov decision processes NeurIPS 2012 Tractable Objectives for Robust Policy Optimization NeurIPS 2012 Weighted Likelihood Policy Search with Model Selection NeurIPS 2012 A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes JMLR 2011 Action-Gap Phenomenon in Reinforcement Learning NeurIPS 2011 Analysis and Improvement of Policy Gradient Estimation NeurIPS 2011 A Non-Parametric Approach to Dynamic Programming NeurIPS 2011 A reinterpretation of the policy oscillation phenomenon in approximate policy iteration NeurIPS 2011 Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery NeurIPS 2011 Exploiting Best-Match Equations for Efficient Reinforcement Learning JMLR 2011 Improving Policy Gradient Estimates with Influence Information ACML 2011 MAP Inference for Bayesian Inverse Reinforcement Learning NeurIPS 2011 Monte Carlo Value Iteration with Macro-Actions NeurIPS 2011 Nonlinear Inverse Reinforcement Learning with Gaussian Processes NeurIPS 2011 Optimal Reinforcement Learning for Gaussian Systems NeurIPS 2011