37 lines
1.9 KiB
Markdown
37 lines
1.9 KiB
Markdown
# Celeste-AI: A Celeste Classic DQL Agent
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This is an attempt to create a deep Q-learning agent that learns to play Celeste Classic.
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## Contents
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- `./resources`: contains files this script requires. Notably, we have an (old) version of PICO-8 that's known to work with this script, and a version of Celeste Classic with telementery and delays called `hackcel.p8`.
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- `ffmpeg.sh`: uses game screenshots to make real-time video of the agent's attempts. Read the script, it's pretty simple.
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- `plot.py`: generates plots from model snapshots. These are placed in `model_data/current/plots/`.
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## Setup
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Before you set up Celeste-AI, you need to prepare PICO-8. See [`resources/README.md`](./resources/README.md)
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This is designed to work on Linux. You will need `xdotool` to send keypresses to the game.
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1. `cd` into this directory
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2. Make and enter a venv
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3. `pip install -e .`
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Once you're set up, you can...
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- `python celeste_ai/train.py` to train a model
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- `python plot.py` to make prediction plots
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- `python test.py` to test a model
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**Before running, be aware of the following:**
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- Only one instance of PICO-8 can be running at a time. See `celeste.py`.
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- `hackcel.p8` captures a screenshot of every frame. PICO-8 will probably place these on your desktop. Since this repo contains a rather old version of PICO-8, there is no way to change where it places screenshots. `train.py` will delete, move, and rename screenshots automatically during training, but you should tell it where your desktop is first.
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- When you start training, a `model_data` directory will be created. It contains the following:
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- `model_archive`: history of the model. Save interval is configured inside `train.py`
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- `screenshots`: contains subdirectories. Each subdirectory contains the frames of one episode. Use `ffmpeg.sh` to turn these into a video.
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- `plots`: generated by `plot.py`. Contains pretty plots. |