Cleaning Room RL Agent

This project builds a Reinforcement Learning (RL) agent to find an optimal floor cleaning method using a power washer in a simulated environment. The agent can move, rotate, and spray the washer to clean dirt, with the reward being the inverse of dirt concentration on the floor.

Features

• Power Washer Simulation: Agent can move and rotate to spray dirt.
• RL Training: Trains an agent using Proximal Policy Optimization (PPO).
• Custom Environment: Built with gymnasium for easy integration with RL algorithms.

Dependencies

Install required packages:

pip install stable-baselines3 torch numpy gymnasium matplotlib

Setup and Usage

1. Training the Agent Run the train_agent() function to train the PPO agent:

python train_agent.py This trains the agent with 4 parallel environments for 1,000,000 timesteps, saving the model as cleaning_agent_final.

2. Evaluating the Agent After training, evaluate the agent's performance:

python evaluate_agent.py

This runs 10 evaluation episodes and prints the average reward.

Feel free to fork and submit pull requests for improvements.

License

This project is licensed under the MIT License.