In this review, we present an analysis of the most used multi-agent reinforcement learning
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …

Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and
diversified applications. These applications belong to the civilian and the military fields. To …

Reinforcement learning (RL) has become a highly successful framework for learning in
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …

In accordance with the rapid proliferation of machine learning (ML) and data management,
ML applications have evolved to encompass all engineering disciplines. Owing to the …

Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …

Principles of typical motion planning algorithms are investigated and analyzed in this paper.
These algorithms include traditional planning algorithms, classical machine learning …

Financial portfolio management is the process of constant redistribution of a fund into
different financial products. This paper presents a financial-model-free Reinforcement …

The popularity of deep reinforcement learning (DRL) applications in economics has
increased exponentially. DRL, through a wide range of capabilities from reinforcement …