Deep RL

Learning to Compose Dynamic Tree Structures for Visual Contexts CVPR 2019 Learning to Design RNA ICLR 2019 Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning CVPR 2019 Learning to Manipulate Object Collections Using Grounded State Representations CoRL 2019 Learning to Paint With Model-Based Deep Reinforcement Learning ICCV 2019 Learning to Perform Local Rewriting for Combinatorial Optimization NeurIPS 2019 Learning To Simulate ICLR 2019 Learning to Understand Goal Specifications by Modelling Reward ICLR 2019 Learning to Walk Via Deep Reinforcement Learning RSS 2019 Learning Transferable Graph Exploration NeurIPS 2019 Learning When Not to Answer: a Ternary Reward Structure for Reinforcement Learning Based Question Answering NAACL 2019 LeTS-Drive: Driving in a Crowd by Learning from Tree Search RSS 2019 Leveraging exploration in off-policy algorithms via normalizing flows CoRL 2019 Leveraging Human Guidance for Deep Reinforcement Learning Tasks IJCAI 2019 Limiting Extrapolation in Linear Approximate Value Iteration NeurIPS 2019 Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning NeurIPS 2019 Logic-Based Sequential Decision-Making AAAI 2019 Long Short-Term Memory with Dynamic Skip Connections AAAI 2019 Look Harder: A Neural Machine Translation Model with Hard Attention ACL 2019 LTL and Beyond: Formal Languages for Reward Function Specification in Reinforcement Learning IJCAI 2019 Machine Translation for Machines: the Sentiment Classification Use Case EMNLP 2019 Making Deep Q-learning methods robust to time discretization ICML 2019 Making History Matter: History-Advantage Sequence Training for Visual Dialog ICCV 2019 MAME : Model-Agnostic Meta-Exploration CoRL 2019 MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning AAAI 2019