In the last few years, reinforcement learning (RL), also called adaptive (or approximate)
dynamic programming, has emerged as a powerful tool for solving complex sequential …

Nowadays, modern engineering systems require sophisticated maintenance strategies to
ensure their correct performance. Maintenance has become one of the most important tasks …

Algorithms for learning the optimal policy of a Markov decision process (MDP) based on
simulated transitions are formulated and analyzed. These are variants of the well-known" …

The fact that I have the opportunity to present a second edition of this monograph is an
indicator for the growing size of the community concerned with agent-based computational …

Microorganisms play an important role in natural material and elemental cycles. Many
common and general biology research techniques rely on microorganisms. Machine …

From Reinforcement Learning to Deep Reinforcement Learning: An Overview | SpringerLink
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A fundamental issue in reinforcement learning algorithms is the balance between
exploration of the environment and exploitation of information already obtained by the agent …

Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult
and time-consuming task that involves much experience and expert knowledge in …

The design and optimization of interplanetary transfer trajectories is one of the most
important tasks during the analysis and design of a deep space mission. Due to their larger∆ …

The electric power industry is confronted with restructuring in which the operation
scheduling is going to be decided based on a competitive market. In this new arrangement …