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Deep RL

3861 directly classified papers

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Papers

Opponent Modeling in Deep Reinforcement Learning ICML 2016
Value Iteration Networks NIPS 2016
Safe and Efficient Off-Policy Reinforcement Learning NIPS 2016
Reinforcement Learning for Visual Object Detection CVPR 2016
Generating Long-term Trajectories Using Deep Hierarchical Networks NIPS 2016
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation NIPS 2016
Strategic Attentive Writer for Learning Macro-Actions NIPS 2016
Deep Exploration via Bootstrapped DQN NIPS 2016
Tree-Structured Reinforcement Learning for Sequential Object Localization NIPS 2016
Improving PAC Exploration Using the Median Of Means NIPS 2016
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning NIPS 2015
Learning Continuous Control Policies by Stochastic Value Gradients NIPS 2015
Off-policy Model-based Learning under Unknown Factored Dynamics ICML 2015
On the Rate of Convergence and Error Bounds for LSTD(λ) ICML 2015
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence ICML 2015
Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games ICML 2015
Universal Value Function Approximators ICML 2015
On Convergence of Emphatic Temporal-Difference Learning COLT 2015
A Deeper Look at Planning as Learning from Replay ICML 2015
Regularized Policy Gradients: Direct Variance Reduction in Policy Gradient Estimation ACML 2015
Sample Efficient Reinforcement Learning with Gaussian Processes ICML 2014
Bayes-Adaptive Simulation-based Search with Value Function Approximation NIPS 2014
Difference of Convex Functions Programming for Reinforcement Learning NIPS 2014
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning NIPS 2014
Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process AISTATS 2014