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 …

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 …

Robotic manipulation challenges, such as grasping and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …

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 …

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 …

These days, taking the revolutionary approach of using clean-slate learning-based designs
to completely replace the classic congestion control schemes for the Internet is gaining …

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

Deep learning has provided new ways of manipulating, processing and analyzing data. It
sometimes may achieve results comparable to, or surpassing human expert performance …

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

Featured Application Assembly tasks with industrial robot manipulators. Abstract Industrial
robot manipulators are playing a significant role in modern manufacturing industries …