/PokemonRedExperiments

Playing Pokemon Red with Reinforcement Learning

Primary LanguageJupyter NotebookMIT LicenseMIT

Train RL agents to play Pokemon Red!

Watch the Video on Youtube!

Running the Pretrained Model Interactively 🎮

🐍 Python 3.10 is recommended. Other versions may work but have not been tested.
You also need to install ffmpeg and have it available in the command line.

  1. Copy your legally obtained Pokemon Red ROM into the base directory. You can find this using google, it should be 1MB. Rename it to PokemonRed.gb if it is not already. The sha1 sum should be ea9bcae617fdf159b045185467ae58b2e4a48b9a, which you can verify by running shasum PokemonRed.gb.
  2. Move into the baselines/ directory:
    cd baselines
  3. Install dependencies:
    pip install -r requirements.txt
    It may be necessary in some cases to separately install the SDL libraries.
  4. Run:
    python run_pretrained_interactive.py

By default the game will terminate after 32K steps, or ~1 hour. You can increase this by adjusting the ep_length variable, but it will also use more memory.

Interact with the emulator using the arrow keys and the a and s keys (A and B buttons).
You can pause the AI's input during the game by editing agent_enabled.txt

Note: the Pokemon.gb file MUST be in the main directory and your current directory MUST be the baselines/ directory in order for this to work.

Training the Model 🏋️

10-21-23: Updated Version!

This version still needs some tuning, but it can clear the first gym in a small fraction of the time and compute resources. It can work with as few as 16 cores and ~20G of RAM. This is the place for active development and updates!

  1. Previous steps 1-3
  2. Run:
    python run_baseline_parallel_fast.py

Original Version

Can be used to reproduce the original results in the video. Not recommended otherwise. This can use up to ~100G of RAM. You can decrease this by reducing the num_cpu or ep_length, but it may affect the results. Also, the model behavior may become degenerate for up to the first 50 training iterations or so before starting to improve. This could likely be fixed with better hyperparameters but I haven't had the time or resources to sweep these.

  1. Previous steps 1-3
  2. Run:
    python run_baseline_parallel.py

Tracking Training Progress 📈

You can view the current state of each emulator, plot basic stats, and compare to previous runs using the VisualizeProgress.ipynb notebook.

Extra 🐜

Map visualization code can be found in visualization/ directory.