← Methods
Reinforcement Learning › Methods ›

Deep RL

3861 directly classified papers

Papers per year

Papers

Information Theoretic Model Predictive Q-Learning L4DC 2020
Feynman-Kac Neural Network Architectures for Stochastic Control Using Second-Order FBSDE Theory L4DC 2020
Learning the model-free linear quadratic regulator via random search L4DC 2020
A Theoretical Analysis of Deep Q-Learning L4DC 2020
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems L4DC 2020
Practical Reinforcement Learning For MPC: Learning from sparse objectives in under an hour on a real robot L4DC 2020
Progressive Relation Learning for Group Activity Recognition CVPR 2020
Predicting Goal-Directed Human Attention Using Inverse Reinforcement Learning CVPR 2020
Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task CVPR 2020
PADS: Policy-Adapted Sampling for Visual Similarity Learning CVPR 2020
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning CVPR 2020
Dynamic Face Video Segmentation via Reinforcement Learning CVPR 2020
Optical Non-Line-of-Sight Physics-Based 3D Human Pose Estimation CVPR 2020
Learning When and Where to Zoom With Deep Reinforcement Learning CVPR 2020
UNAS: Differentiable Architecture Search Meets Reinforcement Learning CVPR 2020
RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real CVPR 2020
Straight to the Point: Fast-Forwarding Videos via Reinforcement Learning Using Textual Data CVPR 2020
Sketch Less for More: On-the-Fly Fine-Grained Sketch-Based Image Retrieval CVPR 2020
Selective Transfer With Reinforced Transfer Network for Partial Domain Adaptation CVPR 2020
PFRL: Pose-Free Reinforcement Learning for 6D Pose Estimation CVPR 2020
Fast Template Matching and Update for Video Object Tracking and Segmentation CVPR 2020
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation CVPR 2020
Model-Based Reinforcement Learning with Value-Targeted Regression L4DC 2020
Generalized Representation Learning Methods for Deep Reinforcement Learning IJCAI 2020
Towards High-Level Intrinsic Exploration in Reinforcement Learning IJCAI 2020