Methods

Reinforcement learning with value advice ACML 2014 Sample Efficient Reinforcement Learning with Gaussian Processes ICML 2014 Scaling Up Approximate Value Iteration with Options: Better Policies with Fewer Iterations ICML 2014 Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process AISTATS 2014 Sparse Multi-Task Reinforcement Learning NeurIPS 2014 Sparse Reinforcement Learning via Convex Optimization ICML 2014 Time-Regularized Interrupting Options (TRIO) ICML 2014 Tracking Adversarial Targets ICML 2014 True Online TD(lambda) ICML 2014 Universal Option Models NeurIPS 2014 Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning JMLR 2014 Weighted importance sampling for off-policy learning with linear function approximation NeurIPS 2014 ABC Reinforcement Learning ICML 2013 Actor-Critic Algorithms for Risk-Sensitive MDPs NeurIPS 2013 Adaptive Step-Size for Policy Gradient Methods NeurIPS 2013 A Gang of Bandits NeurIPS 2013 Aggregating Optimistic Planning Trees for Solving Markov Decision Processes NeurIPS 2013 A message-passing algorithm for multi-agent trajectory planning NeurIPS 2013 A multi-agent control framework for co-adaptation in brain-computer interfaces NeurIPS 2013 Approximate Dynamic Programming Finally Performs Well in the Game of Tetris NeurIPS 2013 Average Reward Optimization Objective In Partially Observable Domains ICML 2013 Bayesian Learning of Recursively Factored Environments ICML 2013 Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search NeurIPS 2013 Bellman Error Based Feature Generation using Random Projections on Sparse Spaces NeurIPS 2013 Better Rates for Any Adversarial Deterministic MDP ICML 2013