Reinforcement Learning

A teacher-student framework to distill future trajectories ICLR 2021 Augmenting Policy Learning with Routines Discovered from a Single Demonstration AAAI 2021 Autonomous Reinforcement Learning via Subgoal Curricula NeurIPS 2021 Average-Reward Learning and Planning with Options NeurIPS 2021 Batch Reinforcement Learning Through Continuation Method ICLR 2021 Bayesian Bellman Operators NeurIPS 2021 Behavioral Cloning from Noisy Demonstrations ICLR 2021 Behavior From the Void: Unsupervised Active Pre-Training NeurIPS 2021 Bridging the Imitation Gap by Adaptive Insubordination NeurIPS 2021 Causal Influence Detection for Improving Efficiency in Reinforcement Learning NeurIPS 2021 Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation COLT 2021 C-Learning: Horizon-Aware Cumulative Accessibility Estimation ICLR 2021 C-Learning: Learning to Achieve Goals via Recursive Classification ICLR 2021 CMAX++ : Leveraging Experience in Planning and Execution using Inaccurate Models AAAI 2021 Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman Problem AAAI 2021 Conservative Safety Critics for Exploration ICLR 2021 Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agent AAAI 2021 Contrastive Active Inference NeurIPS 2021 Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions ICLR 2021 Data-Efficient Reinforcement Learning with Self-Predictive Representations ICLR 2021 Decentralized Q-learning in Zero-sum Markov Games NeurIPS 2021 DeepAveragers: Offline Reinforcement Learning By Solving Derived Non-Parametric MDPs ICLR 2021 Deep Radial-Basis Value Functions for Continuous Control AAAI 2021 DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning AAAI 2021 Discovering and Achieving Goals via World Models NeurIPS 2021