Note: All code and demonstrations are used for the submitted paper:

Ruyu Luo, Wanli Ni, Hui Tian, Julian Cheng, and Kwang-Cheng Chen, "Joint Trajectory and Radio Resource Optimization by Multi-Agent Reinforcement Learning for Autonomous Mobile Robots in Industrial Internet of Things", submitted to IEEE TCom, Dec. 2022.

In this paper, we present the simulation codes of multi-agent reinforcement learning (MARL) with upper-confidence bound (UCB) exploration.

Here are the setting of our simulations.

The visualization of the proposed MARL can be seen in Visual_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.

• Figure_1_reward_comparison (Reward comparison between different algorithms)

  • Centralized_QL

    • RL_brain.py:   Centralized tabular Q-learning agent with e-greedy exploration
    • main.py:  Main code of two robots that train with global information, connections between the environment and learning agents without experience exchange

  • UCB_MARL_environment1

    • RL_brain.py:  One learning agent with upper-confidence bound (UCB) exploration
    • main.py:  Main code of four robots, connections between the environment and learning agents, where two robots without interference at the strictly same environment exchange experience.

  • UCB_MARL_environment2

    • RL_brain.py:  One learning agent with upper-confidence bound (UCB) exploration

    • main.py:  Main code of four robots, connections between the environment and learning agents, where two nearby robots with interference at the same SN deployment exchange experience.

• Figure_2_convergence_comparison (Convergence comparison between different $H$)

  • UCB_MARL

    • RL_brain.py:  One learning agent with upper-confidence bound (UCB) exploration
    • main.py:  Main code of six robots, connections between the environment and learning agents

  • e-greedy_MARL

    • RL_brain.py:  Tabular Q-learning agent with e-greedy exploration

    • main.py:   Main code of two robots that train locally without experience exchange, connections between the environment and learning agents

• Figure_3_robot_trajectory (Robot trajectory under different $\kappa$)

  • RL_brain.py:  One learning agent with upper-confidence bound (UCB) exploration

  • plot_figure.py:   The trajectories with different reward policy

  • [robot trajectory.py](https://github.com/lry-bupt/UCB_MARL/blob/main/Figure_3_robot_trajectory/robot trajectory.py):   Main code of four robots with experience exchange, connections between the environment and learning agents

• Figure_4_relation_between_R_T (Relation between average sum rate and arrival time)

  • RL_brain.py:  One learning agent with upper-confidence bound (UCB) exploration

  • main.py:   Main code of two robots with experience exchange, connections between the environment and learning agents

• Figure_5_R_versus_P (Convergence comparison between different $H$)

  • NOMA

    • RL_brain.py:  One learning agent with upper-confidence bound (UCB) exploration
    • main.py:   Main code of robots with experience exchange, connections between the environment and learning agents that communicate with non-orthogonal multiple access (NOMA)

  • OMA

    • RL_brain.py:  One learning agent with upper-confidence bound (UCB) exploration

    • main.py:    Main code of robots with experience exchange, connections between the environment and learning agents that communicate with orthogonal multiple access (OMA)

[1] R. Luo, W. Ni and H. Tian, "Visualizing Multi-Agent Reinforcement Learning for Robotic Communication in Industrial IoT Networks," admitted by IEEE INFOCOM Demo, Mar. 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.