To ensure the steady navigation for robot stable controls are one of the basic requirements.
Control values selection is highly environment dependent. To ensure reusability of control …

Deep reinforcement learning (DRL) has been employed in numerous applications where
complex decision-making is needed. Robot control in a human environment is an example …

In the field of mobile robot control, the utilization of reinforcement learning methods often
faces the challenge of sparse rewards, resulting in suboptimal control performance. This …

Compared to traditional control methods, deep reinforcement learning (DRL) has the ability
to learn how to solve complex tasks in a dynamic environment simply by collecting …

Navigation for the robot has numerous applications in industries such as agriculture,
couriers, autonomous vehicle, and many more. Navigation seems a simple problem for …

In order to avoid conventional controlling methods which created obstacles due to the
complexity of systems and intense demand on data density, developing modern and more …

This paper introduces a general experimental design scheme for conditions and parameter
settings of robotic arm control under the specific task when using Deep Deterministic Policy …

Deep Reinforcement Learning (DRL) allows agents to make decisions in a specific
environment based on a reward function, without prior knowledge. Adapting …

Deep reinforcement learning has been proposed to train the control agent for robotic arms,
such as Deep Q-Learning (DQN) and Policy Gradient (PG). The approach of Deterministic …

Reinforcement learning (RL) enables agents to take decision based on a reward function.
However, in the process of learning, the choice of values for learning algorithm parameters …