Reinforcement Learning (RL) tasks generally divide into two kinds: continual learning and
episodic learning. The concept of steady state has played a foundational role in the …

The principle of``optimism in the face of uncertainty''underpins many theoretically successful
reinforcement learning algorithms. In this paper we provide a general framework for …

There are many algorithms for regret minimisation in episodic reinforcement learning. This
problem is well-understood from a theoretical perspective, providing that the sequences of …

Recent advances in deep reinforcement learning algorithms have shown great potential and
success for solving many challenging real-world problems, including Go game and robotic …

Recent advancements in reinforcement learning confirm that reinforcement learning
techniques can solve large scale problems leading to high quality autonomous decision …

Non-parametric episodic memory can be used to quickly latch onto high-rewarded
experience in reinforcement learning tasks. In contrast to parametric deep reinforcement …

In many RL applications, once training ends, it is vital to detect any deterioration in the agent
performance as soon as possible. Furthermore, it often has to be done without modifying the …

Recent research has placed episodic reinforcement learning (RL) alongside model-free and
model-based RL on the list of processes centrally involved in human reward-based learning …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

When designing algorithms for finite-time-horizon episodic reinforcement learning problems,
a common approach is to introduce a fictitious discount factor and use stationary policies for …