In this paper, we present an overview of adaptive control by contrasting model‐based
approaches with data‐driven approaches. Indeed, we propose to classify adaptive …

Batch reinforcement learning (RL) is important to apply RL algorithms to many high stakes
tasks. Doing batch RL in a way that yields a reliable new policy in large domains is …

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

This paper introduces a model-based reinforcement learning (MBRL) framework that
incorporates the underlying decision problem in learning the transition model of the …

The choice of the control frequency of a system has a relevant impact on the ability of
reinforcement learning algorithms to learn a highly performing policy. In this paper, we …

We propose and analyze a variant of the classic Polyak–Ruppert averaging scheme,
broadly used in stochastic gradient methods. Rather than a uniform average of the iterates …

We consider the transfer of experience samples (ie, tuples< s, a, s', r>) in reinforcement
learning (RL), collected from a set of source tasks to improve the learning process in a given …

In this paper, we propose and analyze conservative value iteration, which unifies value
iteration, soft value iteration, advantage learning, and dynamic policy programming. Our …

This paper develops a data-driven method for control of partial differential equations (PDE)
based on deep reinforcement learning (RL) techniques. We design a Deep Fitted Q-Iteration …

This paper is about the study of B-FQI, an Approximated Value Iteration (AVI) algorithm that
exploits a boosting procedure to estimate the action-value function in reinforcement learning …