diff --git a/celeste/README.md b/celeste/README.md
index 009d7c2..e9d71ea 100644
--- a/celeste/README.md
+++ b/celeste/README.md
@@ -1,15 +1,37 @@
 # Celeste-AI: A Celeste Classic DQL Agent
 
-This is an attempt to create an agent that learns to play Celeste Classic.
+This is an attempt to create a deep Q-learning agent that learns to play Celeste Classic.
+
+
 
 ## Contents
- - `./resources`: contain files these scripts require. 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`.
+ - `./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`.
+ - `ffmpeg.sh`: uses game screenshots to make real-time video of the agent's attempts. Read the script, it's pretty simple.
+ - `plot.py`: generates plots from model snapshots. These are placed in `model_data/current/plots/`.
 
 
 ## Setup
 
+Before you set up Celeste-AI, you need to prepare PICO-8. See [`resources/README.md`](./resources/README.md)
+
+
+
 This is designed to work on Linux. You will need `xdotool` to send keypresses to the game.
 
 1. `cd` into this directory
 2. Make and enter a venv
-3. `pip install -e .`
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+3. `pip install -e .`
+
+Once you're set up, you can...
+ - `python celeste_ai/train.py` to train a model
+ -  `python plot.py` to make prediction plots
+ - `python test.py` to test a model
+
+
+**Before running, be aware of the following:**
+ - Only one instance of PICO-8 can be running at a time. See `celeste.py`.
+ - `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.
+ - When you start training, a `model_data` directory will be created. It contains the following:
+   - `model_archive`: history of the model. Save interval is configured inside `train.py`
+   - `screenshots`: contains subdirectories. Each subdirectory contains the frames of one episode. Use `ffmpeg.sh` to turn these into a video.
+   - `plots`: generated by `plot.py`. Contains pretty plots.
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