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

We present a modular approach to reinforcement learning that uses a Bayesian
representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set) …

Shaping can be an effective method for improving the learning rate in reinforcement
systems. Previously, shaping has been heuristically motivated and implemented. We …

Potential based reward shaping is a powerful technique for accelerating convergence of
reinforcement learning algorithms. Typically, such information includes an estimate of the …

To make reinforcement learning algorithms run in a reasonable amount of time, it is
frequently necessary to use a well-chosen reward function that gives appropriate “hints” to …

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

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 …

There exist a number of reinforcement learning algorithms which learnby climbing the
gradient of expected reward. Their long-runconvergence has been proved, even in partially …

Most learning algorithms are not invariant to the scale of the signal that is being
approximated. We propose to adaptively normalize the targets used in the learning updates …

Credit assignment in reinforcement learning is the problem of measuring an action's
influence on future rewards. In particular, this requires separating skill from luck, ie …