Li Ning

Jinghao Xin, Jinwoo Kim, Zhi Li et al.

Deep Reinforcement Learning (DRL) has exhibited efficacy in resolving the Local Path Planning (LPP) problem. However, such application in the real world is immensely limited due to the deficient training efficiency and generalization capability of DRL. To alleviate these two issues, a solution named Color is proposed, which consists of an Actor-Sharer-Learner (ASL) training framework and a mobile robot-oriented simulator Sparrow. Specifically, the ASL intends to improve the training efficiency of DRL algorithms. It employs a Vectorized Data Collection (VDC) mode to expedite data acquisition, decouples the data collection from model optimization by multithreading, and partially connects the two procedures by harnessing a Time Feedback Mechanism (TFM) to evade data underuse or overuse. Meanwhile, the Sparrow simulator utilizes a 2D grid-based world, simplified kinematics, and conversion-free data flow to achieve a lightweight design. The lightness facilitates vectorized diversity, allowing diversified simulation setups across extensive copies of the vectorized environments, resulting in a notable enhancement in the generalization capability of the DRL algorithm being trained. Comprehensive experiments, comprising 57 DRL benchmark environments, 32 simulated and 36 real-world LPP scenarios, have been conducted to corroborate the superiority of our method in terms of efficiency and generalization. The code and the video of this paper are accessible at https://github.com/XinJingHao/Color.

Li Ning, Yong Zhang

Collision avoidance is one of the most primary requirement in the decentralized multiagent navigations: while the agents are moving towards their own targets, attentions should be paid to avoid the collisions with the others. In this paper, we introduce the concept of local action cell, which provides for each agent a set of velocities that are safe to perform. Based on the realtime updated local action cells, we propose the LAC-Nav approach to navigate the agent with the properly selected velocity; and furthermore, we coupled the local action cell with an adaptive learning framework, in which the effect of selections are evaluated and used as the references for making decisions in the following updates. Through the experiments for three commonly considered scenarios, we demonstrated the efficiency of the proposed approaches, with the comparison to several widely studied strategies.