Reinforcement Learning

TAAC: Temporally Abstract Actor-Critic for Continuous Control NeurIPS 2021 Tactical Optimism and Pessimism for Deep Reinforcement Learning NeurIPS 2021 Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation CoRL 2021 Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning NeurIPS 2021 TARE: A Hierarchical Framework for Efficiently Exploring Complex 3D Environments RSS 2021 Targeted Data Acquisition for Evolving Negotiation Agents ICML 2021 Task-Agnostic Morphology Evolution ICLR 2021 Task-Optimal Exploration in Linear Dynamical Systems ICML 2021 Taylor Expansion of Discount Factors ICML 2021 Teachable Reinforcement Learning via Advice Distillation NeurIPS 2021 TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL ICML 2021 TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL AAAI 2021 Temporal Difference Learning as Gradient Splitting ICML 2021 Temporal Induced Self-Play for Stochastic Bayesian Games IJCAI 2021 Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks AAAI 2021 Temporally Abstract Partial Models NeurIPS 2021 Temporal Predictive Coding For Model-Based Planning In Latent Space ICML 2021 TempoRL: Learning When to Act ICML 2021 Tesseract: Tensorised Actors for Multi-Agent Reinforcement Learning ICML 2021 THDA: Treasure Hunt Data Augmentation for Semantic Navigation ICCV 2021 The benefits of sharing: a cloud-aided performance-driven framework to learn optimal feedback policies L4DC 2021 The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition NeurIPS 2021 The Difficulty of Passive Learning in Deep Reinforcement Learning NeurIPS 2021 The Emergence of Individuality ICML 2021 The Logical Options Framework ICML 2021