This paper reviews exploration techniques in deep reinforcement learning. Exploration
techniques are of primary importance when solving sparse reward problems. In sparse …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Algorithmic transparency entails exposing system properties to various stakeholders for
purposes that include understanding, improving, and contesting predictions. Until now, most …

Memory and planning rely on learning the structure of relationships among experiences.
Compact representations of these structures guide flexible behavior in humans and animals …

Epistemic Uncertainty is a measure of the lack of knowledge of a learner which diminishes
with more evidence. While existing work focuses on using the variance of the Bayesian …

Recent work on exploration in reinforcement learning (RL) has led to a series of increasingly
complex solutions to the problem. This increase in complexity often comes at the expense of …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

The linearised Laplace method for estimating model uncertainty has received renewed
attention in the Bayesian deep learning community. The method provides reliable error bars …

Abstract The Option Keyboard (OK) was recently proposed as a method for transferring
behavioral knowledge across tasks. OK transfers knowledge by adaptively combining …

In the current landscape of deep learning research, there is a predominant emphasis on
achieving high predictive accuracy in supervised tasks involving large image and language …