Update README.md

README.md

@@ -6,11 +6,11 @@ pipeline_tag: reinforcement-learning
 library_name: stable-baselines3
 ---
 
-Reinforcement-Learning model that utilizes MaskablePPO to guide assembly of a jigsaw puzzle (puzzle pieces with irregular, convex boundaries).
+Reinforcement-Learning model *(puzzler-v0)* that utilizes MaskablePPO to guide assembly of a jigsaw puzzle (puzzle pieces with irregular, convex boundaries).
 
 Code to train/create this custom environment can be found in the "How Puzzling!" [github repo](https://github.com/reeeeemo/how-puzzling).
 
-Initialization Parameters:
+**Initialization Parameters:**
 ```
 def __init__(self, images, seg_model_path, max_steps=100, device="cpu")
 ```
@@ -25,4 +25,9 @@ def __init__(self, images, seg_model_path, max_steps=100, device="cpu")
   - Max number of steps allowed
   - Using MaskablePPO solves the issue of infinite actions, but if you decide to use PPO, ensure `max_steps` is set to the max number of puzzle pieces
 - *device*: **string**
-  - CPU or GPU usage
+  - CPU or GPU usage
+ 
+
+Training data can be found in [events.out.tfevents](./events.out.tfevents.0.500k_5_images_norm_rew) using `tensorboard --logdir .` after downloading the repo. 
+
+The environment was trained with `VecNormalize` from the `stable_baselines3` libary, you can load from [vec_normalize.pkl](./vec_normalize.pkl)