Abstract Deep Reinforcement Learning (DRL) has gained significant adoption in diverse
fields and applications, mainly due to its proficiency in resolving complicated decision …

This study reviews the major developments of Deep Deterministic Policy Gradient (DDPG) in
the field of reinforcement learning. It is innovated by Deep Q-network ideas and can finally …

Recently, reinforcement learning has made remarkable achievements in the fields of natural
science, engineering, medicine and operational research. Reinforcement learning …

Gradient-based reinforcement learning has gained more and more attention. As one of the
most important methods, Deep Deterministic Policy Gradient (DDPG) has achieved …

Recently, Deep Deterministic Policy Gradient (DDPG) is a popular deep reinforcement
learning algorithms applied to continuous control problems like autonomous driving and …

As a famous deep reinforcement learning approach, deep deterministic policy gradient
(DDPG) is able to deal with the problems in the domain of continuous control. To remove …

Recent analyses of certain gradient descent optimization methods have shown that
performance can degrade in some settings-such as with stochasticity or implicit momentum …

Policy gradient methods in deep reinforcement learning have received increasing attention
over the last few years, mainly because of their several successful applications to …

Deep reinforcement learning (DRL) has been increasingly applied to multi-agent domains in
recent years. However, the agent explores the environment randomly, resulting in low …

Abstract Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic
Policy Gradient (TD3) are two classical deterministic policy gradient algorithms. It is worth …