Methods

Learning Large Neighborhood Search Policy for Integer Programming NeurIPS 2021 Learning Markov State Abstractions for Deep Reinforcement Learning NeurIPS 2021 Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation IJCAI 2021 Learning Off-Policy with Online Planning CoRL 2021 Learning One Representation to Optimize All Rewards NeurIPS 2021 Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs NeurIPS 2021 Learning Rewards From Linguistic Feedback AAAI 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-Distribution Reward Shaping with Meta-Learning AAAI 2021 Learning Task Informed Abstractions ICML 2021 Learning Task Sampling Policy for Multitask Learning EMNLP 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 Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics NeurIPS 2021 Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer NeurIPS 2021 Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles CoRL 2021 Learning to Reach Goals via Iterated Supervised Learning ICLR 2021 Learning To Recommend Frame for Interactive Video Object Segmentation in the Wild CVPR 2021 Learning to Recommend from Sparse Data via Generative User Feedback AAAI 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