Methods

Softmax Policy Gradient Methods Can Take Exponential Time to Converge COLT 2021 Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning ICML 2021 Solving Common-Payoff Games with Approximate Policy Iteration AAAI 2021 Solving Compositional Reinforcement Learning Problems via Task Reduction ICLR 2021 Solving Continuous Control with Episodic Memory IJCAI 2021 Solving JumpIN’ Using Zero-Dependency Reinforcement Learning (Student Abstract) AAAI 2021 Solving Large-Scale Extensive-Form Network Security Games via Neural Fictitious Self-Play IJCAI 2021 Solving Partially Observable Stochastic Shortest-Path Games IJCAI 2021 SOON: Scenario Oriented Object Navigation With Graph-Based Exploration CVPR 2021 SOPE: Spectrum of Off-Policy Estimators NeurIPS 2021 Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient ICML 2021 Specializing Versatile Skill Libraries using Local Mixture of Experts CoRL 2021 Spectral Normalisation for Deep Reinforcement Learning: An Optimisation Perspective ICML 2021 Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation NeurIPS 2021 Stabilizing Dynamical Systems via Policy Gradient Methods NeurIPS 2021 Stabilizing Q Learning Via Soft Mellowmax Operator AAAI 2021 Stable-Baselines3: Reliable Reinforcement Learning Implementations JMLR 2021 State Entropy Maximization with Random Encoders for Efficient Exploration ICML 2021 State Relevance for Off-Policy Evaluation ICML 2021 State-Wise Adaptive Discounting from Experience (SADE): A Novel Discounting Scheme for Reinforcement Learning (Student Abstract) AAAI 2021 Stochastic Actor-Executor-Critic for Image-to-Image Translation IJCAI 2021 Stochastic Policy Optimization with Heuristic Information for Robot Learning CoRL 2021 Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret NeurIPS 2021 Storchastic: A Framework for General Stochastic Automatic Differentiation NeurIPS 2021 Strategically efficient exploration in competitive multi-agent reinforcement learning UAI 2021