Robot navigation in a crowd by integrating deep reinforcement learning and online planning

application of reinforcement learning for navigation of a planar snake robot in serpentine locomotion

this article presents an implementation of a reinforcement learning (rl) method for a snake like robot navigation. the paper starts with developing kinematics and dynamics model of a snake robot in serpentine locomotion followed by performing simulation and finishes with actual experimentation. first, gibbs-appell's method is used to obtain the robot dynamics. the robot is also modeled in simme...

Hierarchical Reinforcement Learning for Robot Navigation

For complex tasks, such as manipulation and robot navigation, reinforcement learning (RL) is well-known to be difficult due to the curse of dimensionality. To overcome this complexity and making RL feasible, hierarchical RL (HRL) has been suggested. The basic idea of HRL is to divide the original task into elementary subtasks, which can be learned using RL. In this paper, we propose a HRL archi...

Vision-based Reinforcement Learning for Robot Navigation

In this paper, we present a novel vision-based learning approach for autonomous robot navigation. A hybrid statemapping model, which combines the merits of both static and dynamic state assigning strategies, is proposed to solve the problem of state organization in navigation-learning tasks. Specifically, the continuous feature space, which could be very large in general, is firstly mapped to a...

Automatic Robot Navigation Using Reinforcement Learning

Reinforcement learning-based mobile robot navigation

In recent decades, reinforcement learning (RL) has been widely used in different research fields ranging from psychology to computer science. The unfeasibility of sampling all possibilities for continuous-state problems and the absence of an explicit teacher make RL algorithms preferable for supervised learning in the machine learning area, as the optimal control problem has become a popular su...