amesa-train

Your task is to develop a fully-functional mini-application that leverages the 'amesa-train' package to create a distributed reinforcement learning (RL) training environment for a simple game, such as a basic version of Pac-Man or Snake. This application will demonstrate how to set up a distributed training system using 'amesa-train', allowing multiple agents to learn simultaneously across different machines in a cluster. Here are the steps and features you should consider implementing:

1. **Setup Environment**: Begin by setting up your Python environment with the necessary packages including 'amesa-train'. Ensure you have access to at least two machines for distributed training.
2. **Game Development**: Develop a simple game environment where agents can interact. For instance, a Pac-Man clone where the goal is to eat all the pellets while avoiding ghosts. Alternatively, create a Snake game where the agent navigates through a grid to collect food.
3. **Agent Implementation**: Implement RL agents that can interact with the game environment. Use common RL algorithms like DQN or PPO.
4. **Distributed Training Configuration**: Utilize 'amesa-train' to configure the distributed training setup. Define how agents will communicate and share knowledge across the cluster.
5. **Training Loop**: Create a training loop that runs on multiple machines, each running instances of the game and training agents. Monitor the progress of learning over time.
6. **Evaluation**: After training, evaluate the performance of the trained agents in the game environment. Compare the performance of agents trained in a distributed setup versus a single-machine setup.
7. **Visualization**: Optionally, implement a visualization component to display the training progress and final gameplay of the best-trained agents.

This project aims to showcase the capabilities of 'amesa-train' in enabling efficient distributed training of RL models, highlighting its potential for scaling up AI training tasks.