/FTS_simpel

This repository contains Python scripts that demonstrate how to use Plant Simulation as a learning environment for Reinforcement Learning (RL) algorithms to tackle deadlock situations in Automated Guided Vehicle (AGV) systems. The code is compatible with the Gymnasium and Ray libraries.

Primary LanguagePython

Plant Simulation RL Agents for AGV Systems

Overview

This repository contains Python scripts that demonstrate how to use Plant Simulation as a learning environment for Reinforcement Learning (RL) algorithms to tackle deadlock situations in Automated Guided Vehicle (AGV) systems. The code is compatible with the Gymnasium and Ray libraries.

Files Included

agent_RLlib.py

This file contains the RL agent implemented using Ray's RLlib. The agent can be configured to work with one of the two environments provided in this repository.

env_AGVsimple_multiagent.py

This is a multi-agent environment that simulates an AGV system with deadlock capabilities. It is implemented using the Gymnasium framework and is compatible with Ray's MultiAgentEnv.

env_AGVsimple_gymnasium.py

This is a single-agent environment that also simulates an AGV system with deadlock capabilities. It is implemented using the Gymnasium framework.

Requirements

  • Python 3.x
  • Ray 2.8.0
  • Gymnasium
  • Plant Simulation > 2201

Contributing

If you'd like to contribute, please fork the repository and use a feature branch. Pull requests are warmly welcome.