https://github.com/obvgab/monopoly-ai

A multi-headed double deep-Q learning game of Monopoly in dfdx, Bevy, Naia, and Rust
https://github.com/obvgab/monopoly-ai

Last synced: 11 days ago
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A multi-headed double deep-Q learning game of Monopoly in dfdx, Bevy, Naia, and Rust

• Host: GitHub
• URL: https://github.com/obvgab/monopoly-ai
• Owner: obvgab
• Created: 2022-09-30T01:22:10.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2023-05-05T17:55:32.000Z (over 1 year ago)
• Last Synced: 2024-08-01T19:59:01.408Z (3 months ago)
• Language: Rust
• Homepage:
• Size: 1.51 MB
• Stars: 6
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Monai - Monopoly AI project

## File structure

- Board = Server, shows the game board and handles primary logic (Bevy)

- Player = Human player, provides UI to interact with board (Bevy + Egui)

- Computer = AI player, provides information on the AI and trains model (non-visual)

- Store = Shared information between server and clients

## Process

The game [board](board) is effectively a webserver, so it needs to be started first.

```sh

cd board

cargo run

```

By default, the board will run on the port 1095 (the sum of the ascii characters for MONAI), with a WebRTC port of 1096 for WASM. This can be configured in the file [server.rs](board/src/server.rs#L12). If running clients locally, you can connect using 127.0.0.1, however it is likely that you will have to open the port 1095 in your router. The default code is **MONAI**, [but you can change this.](board/src/server.rs#L22)

For clients, the process depends on whether the player is a [computer](player/computer) or [human](player/human). For a human player, you can either use the WASM client on [my website](https://binarysky.ai/monai-player) or compile and run locally. The WASM client uses wasm-bindgen and the index.html present in the [human's directory](player/human/src/index.html). To run locally, use the same process as the server.

```sh

cd player/human

cargo run

```

From there, you should be able to enter a player name, the authorization code, and the address and port of the board (e.g. 127.0.0.1:1095).

If you want to use the AI, you need to supply command-line arguments. The AI is headless, so no GUI will be present while running/training. It uses a DDQN for reinforcement learning. For settings with the model itself (like the shape, epsilon, optimizer, etc), check [model.rs](player/computer/src/model.rs). To run it, you have to give cargo a few arguments.

```sh

cd player/computer

cargo run -- SERVER CODE NAME NPZ

```

For example, `cargo run -- 127.0.0.1:1095 MONAI Bot1 models/Bot1.npz` would connect a bot player to our localhosted server named Bot1 using Bot1.npz. Some primitive models have been supplied in the repository. At the moment, the model is hardcoded to support a 40 tile board with 4 players, but you can expand this by changing the const values.