Deep RL

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation JMLR 2021 Learning and Planning in Average-Reward Markov Decision Processes ICML 2021 Learning and Planning in Complex Action Spaces ICML 2021 Learning Behaviors through Physics-driven Latent Imagination CoRL 2021 Learning Collaborative Policies to Solve NP-hard Routing Problems NeurIPS 2021 Learning Diverse Policies in MOBA Games via Macro-Goals NeurIPS 2021 Learning Domain Invariant Representations in Goal-conditioned Block MDPs NeurIPS 2021 Learning Infinite-horizon Average-reward MDPs with Linear Function Approximation AISTATS 2021 Learning Large Neighborhood Search Policy for Integer Programming NeurIPS 2021 Learning Markov State Abstractions for Deep Reinforcement Learning NeurIPS 2021 Learning Off-Policy with Online Planning CoRL 2021 Learning One Representation to Optimize All Rewards NeurIPS 2021 Learning Robust State Abstractions for Hidden-Parameter Block MDPs ICLR 2021 Learning Routines for Effective Off-Policy Reinforcement Learning ICML 2021 Learning State Representations from Random Deep Action-conditional Predictions NeurIPS 2021 Learning Task Informed Abstractions ICML 2021 Learning the Non-Differentiable Optimization for Blind Super-Resolution CVPR 2021 Learning to Actively Reduce Memory Requirements for Robot Control Tasks L4DC 2021 Learning To Drive From a World on Rails ICCV 2021 Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles CoRL 2021 Learning To Recommend Frame for Interactive Video Object Segmentation in the Wild CVPR 2021 Learning to Represent Action Values as a Hypergraph on the Action Vertices ICLR 2021 Learning to Reweight Imaginary Transitions for Model-Based Reinforcement Learning AAAI 2021 Learning to Sample with Local and Global Contexts in Experience Replay Buffer ICLR 2021 Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control AAAI 2021