Aiming at the problems of low success rate and slow learning speed of the DDPG algorithm
in path planning of a mobile robot in a dynamic environment, an improved DDPG algorithm …

An improved TD3 algorithm is studied for the low success rate and slow learning speed of
TD3 (Twin Delayed Deep Deterministic Policy Gradients) algorithm in mobile robot path …

Constrained by the numbers of action space and state space, Q-learning cannot be applied
to continuous state space. Targeting this problem, the double deep Q network (DDQN) …

A reinforcement learning‐based continuous action space path planning method for mobile
robots is proposed in this article. First, the kinematic model of the mobile robot is analyzed …

When a traditional Deep Deterministic Policy Gradient (DDPG) algorithm is used in mobile
robot path planning, due to the limited observable environment of mobile robots, the training …

The traditional A* algorithm has several problems in practical applications, such as many
path turning points, redundant nodes, and long running time. it is sometimes impossible to …

A mobile robot path planning method based on improved deep reinforcement learning is
proposed. First, in order to conform to the actual kinematics model of the robot, the …

In order to solve the problems of low learning efficiency and slow convergence speed when
mobile robot uses reinforcement learning method for path planning in complex environment …

Aiming at the problem of low efficiency of mobile robot path planning in complex
environments, based on the traditional A* algorithm and combined with the divide and …

Mobile robotics has a wide range of applications and path planning is key to its realization.
Mobile robots need to explore the environment autonomously to find their destinations. The …