Multi-agent reinforcement learning: A review of challenges and applications
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 …
algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the …
Drone deep reinforcement learning: A review
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 …
diversified applications. These applications belong to the civilian and the military fields. To …
A survey on deep reinforcement learning algorithms for robotic manipulation
Robotic manipulation challenges, such as grasping and object manipulation, have been
tackled successfully with the help of deep reinforcement learning systems. We give an …
tackled successfully with the help of deep reinforcement learning systems. We give an …
Robust reinforcement learning: A review of foundations and recent advances
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 …
Markov decision processes (MDP). Due to the adoption of RL in realistic and complex …
Learning for a robot: Deep reinforcement learning, imitation learning, transfer learning
J Hua, L Zeng, G Li, Z Ju - Sensors, 2021 - mdpi.com
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 …
manipulators can only perform simple tasks such as sorting and packing in a structured …
Multi-agent deep reinforcement learning for multi-robot applications: A survey
J Orr, A Dutta - Sensors, 2023 - mdpi.com
Deep reinforcement learning has produced many success stories in recent years. Some
example fields in which these successes have taken place include mathematics, games …
example fields in which these successes have taken place include mathematics, games …
A review of motion planning algorithms for intelligent robots
Principles of typical motion planning algorithms are investigated and analyzed in this paper.
These algorithms include traditional planning algorithms, classical machine learning …
These algorithms include traditional planning algorithms, classical machine learning …
Deep reinforcement learning for the control of robotic manipulation: a focussed mini-review
Deep learning has provided new ways of manipulating, processing and analyzing data. It
sometimes may achieve results comparable to, or surpassing human expert performance …
sometimes may achieve results comparable to, or surpassing human expert performance …
Variable compliance control for robotic peg-in-hole assembly: A deep-reinforcement-learning approach
CC Beltran-Hernandez, D Petit, IG Ramirez-Alpizar… - Applied Sciences, 2020 - mdpi.com
Featured Application Assembly tasks with industrial robot manipulators. Abstract Industrial
robot manipulators are playing a significant role in modern manufacturing industries …
robot manipulators are playing a significant role in modern manufacturing industries …
A deep reinforcement learning framework for the financial portfolio management problem
Financial portfolio management is the process of constant redistribution of a fund into
different financial products. This paper presents a financial-model-free Reinforcement …
different financial products. This paper presents a financial-model-free Reinforcement …