In this paper, we introduce a novel method for enhancing the effectivene...
Recently, the Successor Features and Generalized Policy Improvement (SF ...
Bayesian deep learning seeks to equip deep neural networks with the abil...
We propose Algorithm Distillation (AD), a method for distilling reinforc...
Learned models of the environment provide reinforcement learning (RL) ag...
High-quality estimates of uncertainty and robustness are crucial for num...
We study reinforcement learning (RL) with no-reward demonstrations, a se...
Out-of-training-distribution (OOD) scenarios are a common challenge of
l...
In this paper, we develop a metric designed to assess and rank uncertain...
Generalization across environments is critical to the successful applica...
Evaluation of Bayesian deep learning (BDL) methods is challenging. We of...
In this thesis, we develop a comprehensive account of the expressive pow...
Before deploying autonomous agents in the real world, we need to be conf...
Multi-agent learning is a promising method to simulate aggregate competi...