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

In continuing tasks, average-reward reinforcement learning may be a more appropriate
problem formulation than the more common discounted reward formulation. As usual …

Model-free episodic reinforcement learning problems define the environment reward with
functions that often provide only sparse information throughout the task. Consequently …

We study the reward-free reinforcement learning framework, which is particularly suitable for
batch reinforcement learning and scenarios where one needs policies for multiple reward …

Many practical applications of reinforcement learning require agents to learn from sparse
and delayed rewards. It challenges the ability of agents to attribute their actions to future …

Reinforcement learning (RL) provides a naturalistic framing for learning through trial and
error, which is appealing both because of its simplicity and effectiveness and because of its …

A key challenge in many reinforcement learning problems is delayed rewards, which can
significantly slow down learning. Although reward shaping has previously been introduced …

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 …

While reinforcement learning (RL) provides a framework for learning through trial and error,
translating RL algorithms into the real world has remained challenging. A major hurdle to …

Applying conventional reinforcement to complex domains requires the use of an overly
simplified task model, or a large amount of training experience. This problem results from the …