Deploying reinforcement learning (RL) systems requires robustness to uncertainty and
model misspecification, yet prior robust RL methods typically only study noise introduced …

Video instance segmentation requires classifying segmenting and tracking every object
across video frames. Unlike existing approaches that rely on masks boxes or category labels …

Dyna-style model-based reinforcement learning contains two phases: model rollouts to
generate sample for policy learning and real environment exploration using current policy …

We present Premier-TACO, a multitask feature representation learning approach designed
to improve few-shot policy learning efficiency in sequential decision-making tasks. Premier …

Off-policy reinforcement learning (RL) has achieved notable success in tackling many
complex real-world tasks, by leveraging previously collected data for policy learning …

This study investigates the loss of generalization ability in neural networks, revisiting warm-
starting experiments from Ash & Adams. Our empirical analysis reveals that common …

Deep neural networks provide Reinforcement Learning (RL) powerful function
approximators to address large-scale decision-making problems. However, these …

The varying significance of distinct primitive behaviors during the policy learning process
has been overlooked by prior model-free RL algorithms. Leveraging this insight, we explore …

One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency.
Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning …

Visual reinforcement learning (RL) has made significant progress in recent years, but the
choice of visual feature extractor remains a crucial design decision. This paper compares …