most methods still rely on experts'knowledge.To address these issues,this paper proposes a penetration path planning method based on reinforcement learning with episodic memory.First,the penetration testing problem is formally described in terms of reinforcement learning.To speed up the training process ...
A robot path planning algorithm based on reinforcement learning is proposed. The algorithm discretizes the information of obstacles around the mobile robot and the direction information of target points obtained by LiDAR into finite states, then reasonably designs the number of environment model and sta...
We introduce a new multi-UAV Mobile Edge Computing platform, which aims to provide better Quality-of-Service and path planning based on reinforcement learning to address these issues. The contributions of our work include: 1) optimizing the quality of service for mobile edge computing and path ...
This research proposed a novel approach of ship path planning based on the Q-learning algorithm. To make the approach practical, the first-order Nomoto equation was used, by which the following position and heading angle of the ship can be inferred according to the present position, rudder angl...
Path planning in dynamic environment has been the hot research direction. This paper considers a new dynamic environment—the obstacles are randomly distributed in the environment, and all of the obstacles will be distributed randomly again after robot’
This paper introduce a method of robot path planning based on reinforcement learning,which aimed at Markovian decision process. In this paper, we introduce the basic concept, principle and the method of reinforcement learning and some other algorithms.Then,we do research from single robotrsquo;s ...
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 continuous environmental state space and discrete action state space are designed. In addition, an improved deep Q-network (DQ...
Local path planning and obstacle avoidance in complex environments are two challenging problems in the research of intelligent robots. In this study, we develop a novel approach grounded in deep distributional reinforcement learning to address these challenges. Within this methodology, agents instantiated ...
The deep reinforcement learning (DRL) model is a single-step algorithm, so the dynamic environments will not affect its running time consumption, which is superior to the traditional path planning algorithms in terms of running time consumption. However, the DRL model will face the problem of ...
Yi Yang, et al. Local Path Planning Method of the Self-propelled Model Based on Reinforcement Learning in Complex Conditions 334 during the voyage. θ guidance is the order from the local path planner as the heading angle. Under normal circumstances, ...