This repository contains the Python implementation of our submitted paper titled "Deep Reinforcement Learning Enables Joint Trajectory and Communication in Internet of Robotic Things" .

[Installation] [Installation] [Usage]

We learn the multi-agent actor-critic deep reinforcement learning (MAAC-DRL) algorithms to reduce the decoding error rate and arriving time of robots in industrial Internet of Robotic Things (IoRT) with the requirements of ultra-reliable and low-latency communications.

Here are the settings of the considered IoRT environment.

For more details and simulation results, please check our paper.

Dependencies can be installed by Conda:

For example to install env used for IoRT environments with URLLC requirements:

conda env create -f environment/environment.yml URLLC
conda activate URLLC

Then activate it by

conda activate URLLC

To run on atari environment, please further install the considered environment by

pip install -r environment/requirements.txt

Here are the parameters of our simulations.

• 'MA-DDPG_main.py' (Main functions and MDP transitions of MA-DDPG)
• 'MA-PPO_main.py' (Main functions and MDP transitions of MA-PPO)
• 'MA-SAC_main.py' (Main functions and MDP transitions of MA-SAC)
• 'MA-TD3_core.py' (MLP operators of MA-TD3)
• 'MA-TD3_main.py' (Main functions and MDP transitions of MA-TD3)

• 'environment.yaml' (Conda environmental document)
• 'requirements.txt' (Pip environmental document)

• 'FIGURE_1.m' (Reward comparison under different MA-DRL algorithms)
• 'FIGURE_2.m' (Robots' trajectory comparison under different reward settings)
• 'FIGURE_3.m' (Average decoding error probability under different clustering and multiple access scheme)
• 'FIGURE_4.m' (Objective function under different environmental settings)
• 'FIGURE_5.m' (Arriving time under different environmental settings)

• 'AABB_plot.m' (Construct a radio map based on the deployment of obstacles and intersection detection)
• 'map_data.mat' (Raw data of the built radio map)