Uncertainty is ubiquitous in games, both in the agents playing games and often in the games
themselves. Working with uncertainty is therefore an important component of successful …

Legged locomotion holds the premise of universal mobility, a critical capability for many real-
world robotic applications. Both model-based and learning-based approaches have …

Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …

In this work we identify the dormant neuron phenomenon in deep reinforcement learning,
where an agent's network suffers from an increasing number of inactive neurons, thereby …

Deep reinforcement learning is a promising approach to learning policies in uncontrolled
environments that do not require domain knowledge. Unfortunately, due to sample …

Abstract In Reinforcement Learning (RL), enhancing sample efficiency is crucial, particularly
in scenarios when data acquisition is costly and risky. In principle, off-policy RL algorithms …

Legged robots have enormous potential in their range of capabilities, from navigating
unstructured terrains to high-speed running. However, designing robust controllers for highly …

A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty
Source's Perspective Page 1 A Survey on Uncertainty Quantification Methods for Deep Neural …

Unsupervised pre-training strategies have proven to be highly effective in natural language
processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds …

Recent years have witnessed significant advancements in offline reinforcement learning
(RL), resulting in the development of numerous algorithms with varying degrees of …