SG-D3QN

This repository contains the codes for our paper, which is in submission.

Abstract

We propose a graph-based deep reinforcement learning method, \textcolor{blue}{social graph-based double dueling deep Q-network (SG-D3QN)}, that (i) introduces a social attention mechanism to extract an efficient graph representation for the crowd-robot state, (ii) extends the previous state value approximator to a state-action value approximator, (iii) \textcolor{blue}{further optimizes the state-action value approximator with simulated experiences generated by the learned environment model, and (iv) then proposes a human-like decision-making process by integrating model-free reinforcement learning and online planning.} Experimental results indicate that our approach helps the robot understand the crowd and achieves a high success rate of more than 0.99 in the crowd navigation task. Compared with previous state-of-the-art algorithms, our approach achieves better performance. Furthermore, with the human-like decision-making process, our approach incurs less than half of the computational cost.

Method Overview

Setup

1. Install Python-RVO2 library
2. Install socialforce library
3. Install crowd_sim and crowd_nav into pip

pip install -e .

Getting Started

This repository are organized in two parts: crowd_sim/ folder contains the simulation environment and crowd_nav/ folder contains codes for training and testing the policies. Details of the simulation framework can be found here. Below are the instructions for training and testing policies, and they should be executed inside the crowd_nav/ folder.

1. Train a policy.

python train.py --policy tree-search-rl

2. Test policies with 1000 test cases.

python test.py --model_dir data/output 

3. Run policy for one episode and visualize the result.

python test.py --policy tree-search-rl --model_dir data/output --phase test --visualize --test_case 0

Note that run_experiments_main.sh in built to train all policy automatically.

Trajectory Diagram

Simple Scenario	Complex Scenario

Attention Weights

Simple Scenario	Complex Scenario

Video Demo

Holonomic Robot in Simple Scenarios	Rotation-Contrained Robot in Simple Scenarios
Holonomic Robot in Complex Scenarios	Rotation-Contrained Robot in Complex Scenarios

Acknowledge

This work is based on CrowdNav and RelationalGraphLearning. The authors thank Changan Chen, Yuejiang Liu, Sven Kreiss, Alexandre Alahi, Sha Hu, Payam Nikdel, Greg Mori, Manolis Savva for their works.