/Coach-RL

This paper proposes an end-to-end approach for the coaching task based on Reinforcement Learning (RL)

Primary LanguageJupyter Notebook

Coach-RL

Repository of the paper An analysis of Reinforcement Learning applied to Coach task in IEEE Very Small Size Soccer to be published in Latin American Robotics Symposium 2020

This project analyze the match situation and choose the best strategy that can be composed of a Goalkeeper, a defender, and an attacker.

Example of learned behaviors: efficient position switching

Installation

  • Install FIRASim

  • git clone https://github.com/RC-Dynamics/Coach-RL.git

  • pip install -e .

  • Build your vss-software

  • Copy vss-software/src/Config to examples

  • Copy your builded agent to gym_coach_vss/bin

  • run examples/sample_coach.py

ps. CoachEnv takes as argument "sim_path". This argument indicates where your FIRASim binarie is. The default will take it as '/home/$USER/FIRASim/bin/FIRASim'.

ToDo

  • Instructons to the comunication protocol.
  • Improve README