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Maze 3D Collaborative Learning on shared task


Contact: [email protected]


Maze 3D game from: https://github.com/amengede/Marble-Maze

Reinforcement Learning (RL) Agent: Soft Actor Critic (SAC)

Learn the task collaboratively

  • (Recommended) create a python virtual environment

      python3 -m venv env
      source venv/bin/activate
      pip install -r requirements.txt
    
  • Adjust the hyperparameters in the config_sac.yaml or the config_human.yaml file

    • Note 1: Only discrete SAC is compatible with the game so far
    • Note 2: There are already configuration files set up based on the PETRA 21 short paper in the config/ directory
  • Control

    • Use left and right arrows to control the tilt of the tray around its vertical(y) axis
    • Press once the spacekey to pause and a second time to resume
    • Press q to exit the experiment.
  • Get Familiar with the game

    • Run the command bellow to play 10 trials with the game controlling both DOF (up, down , left, right) with the keyboard's arrows.

        python maze3d_human_only_test.py config/config_human_test.yaml
      
  • Train

    • Notes before training:

      • substitute the participant_name in each config file with your own
      • the program will automatically create an identification number after your name on each folder name created
    • With the RL agent:

       python sac_maze3d_train.py config/config_sac_<experiment_specifications>.yaml
      
    • With a Second human:

      python sac_maze3d_train.py config/config_human.yaml  
      

Game Overview

Game

Game Specifications
  • Board

    • Square with side size: 320 pixels
    • Board Area: 102400 pixels
    • Free Board Area (excluding walls): 58368 pixels
  • Wall Cubes

    • Cubes with edge size: 32 pixels
    • Cube bottom Area: 1024 pixels
  • Ball

    • sphere with radius ρ2: 16 pixels
    • ball Area: 805 pixels
  • Goal

    • Circle with center c1:(-104, -104) and radius ρ1: 22 pixels
    • goal Area: 1521 pixels
  • Ratios

    • Goal - Free Board: ~ 2.6%
    • Ball - Goal: ~ 53%
  • Goal reached if ball's whole projection area on the board falls in the goal's area Game

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Human-Agent Collaborative Deep Reinforcement Learning

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