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.