Design and Experimental Validation of Deep Reinforcement Learning-Based Fast Trajectory Planning and Control for Mobile Robot in Unknown Environment.

This article is concerned with the problem of planning optimal maneuver trajectories and guiding the mobile robot toward target positions in uncertain environments for exploration purposes. A hierarchical deep learning-based control framework is proposed which consists of an upper level motion planning layer and a lower level waypoint tracking layer. In the motion planning phase, a recurrent deep neural network (RDNN)-based algorithm is adopted to predict the optimal maneuver profiles for the mobile robot.

Distributed Neural Networks Training for Robotic Manipulation With Consensus Algorithm.

In this article, we propose an algorithm that combines actor-critic-based off-policy method with consensus-based distributed training to deal with multiagent deep reinforcement learning problems. Specifically, convergence analysis of a consensus algorithm for a type of nonlinear system with a Lyapunov method is developed, and we use this result to analyze the convergence properties of the actor training parameters and the critic training parameters in our algorithm. Through the convergence analysis, it can be verified that all agents will converge to the same optimal model as the training time goes to infinity.