Deep RL

Integrating kinematics and environment context into deep inverse reinforcement learning for predicting off-road vehicle trajectories CoRL 2018 Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation CoRL 2018 IQA: Visual Question Answering in Interactive Environments CVPR 2018 Is Q-Learning Provably Efficient? NeurIPS 2018 Iterative Value-Aware Model Learning NeurIPS 2018 Keeping in Touch with Collaborative UAVs: A Deep Reinforcement Learning Approach IJCAI 2018 Latent Space Policies for Hierarchical Reinforcement Learning ICML 2018 Learning Abstract Options NeurIPS 2018 Learning a Policy for Opportunistic Active Learning EMNLP 2018 Learning by Playing Solving Sparse Reward Tasks from Scratch ICML 2018 Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations RSS 2018 Learning Deep Sketch Abstraction CVPR 2018 Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal CoRL 2018 Learning Environmental Calibration Actions for Policy Self-Evolution IJCAI 2018 Learning Globally Optimized Object Detector via Policy Gradient CVPR 2018 Learning Intelligent Dialogs for Bounding Box Annotation CVPR 2018 Learning Sentence Representations over Tree Structures for Target-Dependent Classification NAACL 2018 Learning Temporal Point Processes via Reinforcement Learning NeurIPS 2018 Learning the Reward Function for a Misspecified Model ICML 2018 Learning to Explore via Meta-Policy Gradient ICML 2018 Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks CVPR 2018 Learning Unmanned Aerial Vehicle Control for Autonomous Target Following IJCAI 2018 Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning NeurIPS 2018 Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator ICML 2018 Linear Stochastic Approximation: How Far Does Constant Step-Size and Iterate Averaging Go? AISTATS 2018