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 …

Common approaches for task-agnostic exploration learn tabula-rasa--the agent assumes
isolated environments and no prior knowledge or experience. However, in the real world …

Robots often face situations where grasping a goal object is desirable but not feasible due to
other present objects preventing the grasp action. We present a deep Reinforcement …

In realistic sparse reward tasks, existing theoretical methods cannot be effectively applied
due to the low sampling probability ofrewarded episodes. Profound research on methods …

Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely
on getting feedback via extrinsic rewards to train the agent, and in situations where this …

Exploration in reinforcement learning (RL) remains an open challenge. RL algorithms rely
on observing rewards to train the agent, and if informative rewards are sparse the agent …

In this thesis, we explore two distinct topics. The first part of the thesis delves into efficient
exploration in multi-task bandit models and model-free exploration in large Markov decision …

A popular computer puzzle, the game of Minesweeper requires its human players to have a
mix of both luck and strategy to succeed. Analyzing these aspects more formally, in our …

Deep learning holds promise for learning complex patterns from data, which is especially
useful when the input or output space is large. In robot learning, both the input (images or …