Reinforcement Learning

Learning in Nonzero-Sum Stochastic Games with Potentials ICML 2021 Learning in two-player zero-sum partially observable Markov games with perfect recall NeurIPS 2021 Learning Knowledge Graph-based World Models of Textual Environments NeurIPS 2021 Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation CoRL 2021 Learning Large Neighborhood Search Policy for Integer Programming NeurIPS 2021 Learning Markov State Abstractions for Deep Reinforcement Learning NeurIPS 2021 Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning NeurIPS 2021 Learning Multimodal Rewards from Rankings CoRL 2021 Learning Multi-Stage Tasks with One Demonstration via Self-Replay CoRL 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 Proofs of Motion Planning Infeasibility RSS 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 Space Partitions for Path Planning NeurIPS 2021 Learning Stabilizing Controllers for Unstable Linear Quadratic Regulators from a Single Trajectory L4DC 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