DeepAI AI Chat
Log In Sign Up

Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

by   Samaneh Hosseini Semnani, et al.
Ryerson University
Isfahan University of Technology

This paper introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision-free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3C-CADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50 extra time to reach goal than FMP.


An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios

Intelligent robots provide a new insight into efficiency improvement in ...

Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder

In this paper, we present a hierarchical path planning framework called ...

Integrating Deep Reinforcement and Supervised Learning to Expedite Indoor Mapping

The challenge of mapping indoor environments is addressed. Typical heuri...

A Little More, a Lot Better: Improving Path Quality by a Simple Path Merging Algorithm

Sampling-based motion planners are an effective means for generating col...

Cooperative Planning for an Unmanned Combat Aerial Vehicle Fleet Using Reinforcement Learning

In this study, reinforcement learning (RL)-based centralized path planni...

Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR

In this paper, a proactive dynamic spectrum sharing scheme between 4G an...