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 …

In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

Many practical applications of reinforcement learning constrain agents to learn from a fixed
batch of data which has already been gathered, without offering further possibility for data …

We introduce an exploration bonus for deep reinforcement learning methods that is easy to
implement and adds minimal overhead to the computation performed. The bonus is the error …

Reinforcement learning promises to solve complex sequential-decision problems
autonomously by specifying a high-level reward function only. However, reinforcement …

In value-based reinforcement learning methods such as deep Q-learning, function
approximation errors are known to lead to overestimated value estimates and suboptimal …

Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …

Offline Reinforcement Learning (RL) aims to learn policies from previously collected
datasets without exploring the environment. Directly applying off-policy algorithms to offline …

A grand challenge in reinforcement learning is intelligent exploration, especially when
rewards are sparse or deceptive. Two Atari games serve as benchmarks for such hard …