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Update README with project info

README.md

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 license: openrail
+pipeline_tag: reinforcement-learning
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+
+# EfficientZero Remastered
+
+This repo contains the pre-trained models for the EfficientZero Remastered
+project from Gigglebit Studios, a project to stabilize the training process
+for the state of the art EfficientZero model.
+
+* [Training source code](https://github.com/steventrouble/EfficientZero)
+* [About the project](https://www.gigglebit.net/blog/efficientzero.html)
+* [About EfficientZero](https://arxiv.org/abs/2111.00210)
+* [About Gigglebit](https://www.gigglebit.net/)
+
+Huge thanks to [Stability AI](https://stability.ai/) for providing the compute
+for this project!
+
+---
+
+## How to use these files
+
+Download the model that you want to test, then run test.py to test the model.
+
+_Note: We've only productionized the training process. If you want to use these
+for inference in production, you'll need to write your own inference logic.
+If you do, send us a PR and we'll add it to the repo!_
+
+Files are labeled as follows:
+
+```
+{gym_env}-s{seed}-e{env_steps}-t{train_steps}
+```
+
+Where:
+*   `gym_env`: The string ID of the gym environment this model was trained on.
+    E.g. Breakout-v5
+*   `seed`: The seed that was used to train this model. Usually 0.
+*   `env_steps`: The total number of steps in the environment that this model
+    observed, usually 100k.
+*   `train_steps`: The total number of training epochs the model underwent.
+
+Note that `env_steps` can differ from `train_steps` because the model can
+continue fine-tuning using its replay buffer. In the paper, the last 20k
+epochs are done in this manner. This isn't necessary outside of benchmarks
+and in theory better performance should be attainable by getting more samples
+from the env.