Note: All code and demonstrations are used for our submitted papers:

Ruyu Luo, Wanli Ni, and Hui Tian, "Visualizing Multi-Agent Reinforcement Learning for Robotic Communication in Industrial IoT Networks," IEEE INFOCOM Demo, May. 2022.

In this paper, we present the simulation and visualization of multi-agent reinforcement learning (MARL).

• Here are four demonstrations for different stages in the MARL training process.
  • the beginning of training
  • 800 rounds of training  
  • 1600 rounds of training  
  • the end of training    

• Here is a simple introduction to the code used in our paper.

  • visualization tool

    • visualization tool.py:   Main code of four robots, connections between the environment and learning agents
    • RL_brain.py:   One learning agent with upper-confidence bound (UCB) exploration
    • plot_figure.py:   Reward convergence figure

  • MARL convergence

    • MARL convergence.py:   Main code of six robots with experience exchange, connections between the environment and learning agents & the visualization of real-time system status
    • RL_brain.py:   One learning agent with upper-confidence bound (UCB) exploration

  • robot trajectory

    • robot_trajectory.py:   Main code of two robots, connections between the environment and learning agents
    • RL_brain.py:   One learning agent with upper-confidence bound (UCB) exploration
    • plot_figure.py:   The trajectories with different reward policy

[1] D. C. Nguyen et al., “6G Internet of Things: A Comprehensive Survey,” IEEE Internet of Things J., vol. 9, no. 1, pp. 359-383, Jan. 2022.

[2] R. Luo, H. Tian and W. Ni, “Communication-Aware Path Design for Indoor Robots Exploiting Federated Deep Reinforcement Learning,” in Proc. IEEE PIMRC, Helsinki, Finland, Sept. 2021, pp. 1197-1202.

[3] C. Jin et al., “Is Q-learning Provably Efficient?” in Proc. NeurIPS, Montr´eal, Canada, Dec. 2018, pp. 4868-4878.