Formal exploration approaches in model-based reinforcement learning estimate the
accuracy of the currently learned model without consideration of the empirical prediction …

We present a new model-based algorithm for reinforcement learning (RL) which consists of
explicit exploration and exploitation phases, and is applicable in large or infinite state …

Abstract Model-based reinforcement learning, in which a model of the environment's
dynamics is learned and used to supplement direct learning from experience, has been …

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) …

In the model-based policy search approach to reinforcement learning (RL), policies are
found using a model (or" simulator") of the Markov decision process. However, for high …

Potential-based shaping was designed as a way of introducing background knowledge into
model-free reinforcement-learning algorithms. By identifying states that are likely to have …

Recent model-free reinforcement learning algorithms have proposed incorporating learned
dynamics models as a source of additional data with the intention of reducing sample …

Recent advancements in model-based reinforcement learning have shown that the
dynamics of many structured domains (eg DBNs) can be learned with tractable sample …

Reinforcement learning algorithms typically consider discrete-time dynamics, even though
the underlying systems are often continuous in time. In this paper, we introduce a model …

In many practical applications of RL, it is expensive to observe state transitions from the
environment. For example, in the problem of plasma control for nuclear fusion, computing …