Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

This paper is concerned with a new discrete-time policy iteration adaptive dynamic
programming (ADP) method for solving the infinite horizon optimal control problem of …

We present a novel game-theoretic (GT) and reinforcement learning (RL) framework for
computational offloading in the mobile edge computing (MEC) network operated by multiple …

The number of Electric Vehicle (EV) owners is expected to significantly increase in the near
future, since EVs are regarded as valuable assets both for transportation and energy storage …

Abstract Temporal-difference learning (TD), coupled with neural networks, is among the
most fundamental building blocks of deep reinforcement learning. However, due to the …

Reinforcement Learning (RL) has been effectively used to solve complex problems given
careful design of the problem and algorithm parameters. However standard RL approaches …

We consider a public blockchain realized in the mobile edge computing (MEC) network,
where the blockchain miners compete against each other to solve the proof-of-work puzzle …

In this paper, we study a decentralized deep neural network (DNN) task partitioning and
offloading control problem for a multi-access edge computing (MEC) system with multiple …

In the framework of Markov Decision Processes, we consider linear off-policy learning, that
is the problem of learning a linear approximation of the value function of some fixed policy …