The impact of internal combustion engine-powered automobiles on climate change due to
emissions and the depletion of fossil fuels has contributed to the progress of electrified …

This paper examines the current state of the art of hydrogen refuelling stations-based
production and storage systems for fuel cell hybrid electric vehicles (FCHEV). Nowadays …

Many reinforcement learning algorithms use trajectories collected from the execution of one
or more policies to propose a new policy. Because execution of a bad policy can be costly or …

Policy evaluation is an essential step in most reinforcement learning approaches. It yields a
value function, the quality assessment of states for a given policy, which can be used in a …

Abstract We present POLITEX (POLicy ITeration with EXpert advice), a variant of policy
iteration where each policy is a Boltzmann distribution over the sum of action-value function …

Reinforcement learning (RL) has been successfully used to solve many continuous control
tasks. Despite its impressive results however, fundamental questions regarding the sample …

In this paper, we analyze the convergence rate of the gradient temporal difference learning
(GTD) family of algorithms. Previous analyses of this class of algorithms use ODE …

Abstract Specifying a Reinforcement Learning (RL) task involves choosing a suitable
planning horizon, which is typically modeled by a discount factor. It is known that applying …

Many challenging real-world control problems require adaptation and learning in the
presence of uncertainty. Examples of these challenging domains include aircraft adaptive …

We study two regularization-based approximate policy iteration algorithms, namely REG-
LSPI and REG-BRM, to solve reinforcement learning and planning problems in discounted …