Methods

Return-Based Contrastive Representation Learning for Reinforcement Learning ICLR 2021 Revisiting Peng’s Q($λ$) for Modern Reinforcement Learning ICML 2021 Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research ICML 2021 Revisiting the Weaknesses of Reinforcement Learning for Neural Machine Translation NAACL 2021 Reward based Hebbian Learning in Direct Feedback Alignment (Student Abstract) AAAI 2021 Reward Biased Maximum Likelihood Estimation for Reinforcement Learning L4DC 2021 Reward-Constrained Behavior Cloning IJCAI 2021 Reward Identification in Inverse Reinforcement Learning ICML 2021 Reward is enough for convex MDPs NeurIPS 2021 RewardsOfSum: Exploring Reinforcement Learning Rewards for Summarisation ACL 2021 Rewards with Negative Examples for Reinforced Topic-Focused Abstractive Summarization EMNLP 2021 Risk-Averse Learning by Temporal Difference Methods with Markov Risk Measures JMLR 2021 Risk-Averse Offline Reinforcement Learning ICLR 2021 Risk-Aware Transfer in Reinforcement Learning using Successor Features NeurIPS 2021 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning ICML 2021 Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach ICML 2021 RL for Latent MDPs: Regret Guarantees and a Lower Bound NeurIPS 2021 RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract) AAAI 2021 RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem NeurIPS 2021 RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents NeurIPS 2021 RMM: Reinforced Memory Management for Class-Incremental Learning NeurIPS 2021 RMP2: A Structured Composable Policy Class for Robot Learning RSS 2021 Robust Adversarial Imitation Learning via Adaptively-Selected Demonstrations IJCAI 2021 Robust Asymmetric Learning in POMDPs ICML 2021 Robust Bandit Learning with Imperfect Context AAAI 2021