← Methods
Reinforcement Learning › Methods ›

Deep RL

3861 directly classified papers

Papers per year

Papers

A Non-Parametric Approach to Dynamic Programming NIPS 2011
Convergent Fitted Value Iteration with Linear Function Approximation NIPS 2011
Analysis and Improvement of Policy Gradient Estimation NIPS 2011
Selecting the State-Representation in Reinforcement Learning NIPS 2011
A Reinforcement Learning Theory for Homeostatic Regulation NIPS 2011
Speedy Q-Learning NIPS 2011
Infinite-Horizon Model Predictive Control for Periodic Tasks with Contacts RSS 2011
Reinforcement Learning using Kernel-Based Stochastic Factorization NIPS 2011
Environmental statistics and the trade-off between model-based and TD learning in humans NIPS 2011
Variance Reduction in Monte-Carlo Tree Search NIPS 2011
A reinterpretation of the policy oscillation phenomenon in approximate policy iteration NIPS 2011
Blending Autonomous Exploration and Apprenticeship Learning NIPS 2011
Optimal Reinforcement Learning for Gaussian Systems NIPS 2011
Monte Carlo Value Iteration with Macro-Actions NIPS 2011
TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning NIPS 2011
Nonlinear Inverse Reinforcement Learning with Gaussian Processes NIPS 2011
Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery NIPS 2011
Learning Policy Improvements with Path Integrals AISTATS 2010
A Convergent Online Single Time Scale Actor Critic Algorithm JMLR 2010
Variable Impedance Control - A Reinforcement Learning Approach RSS 2010
Basis Construction from Power Series Expansions of Value Functions NIPS 2010
Predictive State Temporal Difference Learning NIPS 2010
Double Q-learning NIPS 2010
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks NIPS 2010
A Reduction from Apprenticeship Learning to Classification NIPS 2010