Update README.md

README.md

@@ -40,11 +40,11 @@ configs:
 - config_name: default
   data_files:
   - split: train
-    path: data/train.parquet
+    path: data/full_dataset/train.parquet
   - split: test
-    path: data/test.parquet
+    path: data/full_dataset/test.parquet
   - split: validation
-    path: data/validation.parquet
+    path: data/full_dataset/validation.parquet
 language:
 - en
 tags:
@@ -88,7 +88,10 @@ It is further recommended that any bot you make with this as initial training sh
 
 ## Dataset Structure
 
+### Full Dataset
+
 This dataset stores evaluations for game positions in Ultimate Tic-Tac-Toe. Each data point represents a specific game state and its associated evaluation metrics.
+The full dataset stores all of the refactored data from the original dataset
 
 **Features:**
 
@@ -126,7 +129,25 @@ This dataset stores evaluations for game positions in Ultimate Tic-Tac-Toe. Each
         * **`symbol` (int64):**
             * Indicates the symbol placed on the board (e.g., X = 1, 0 = 2).
          
+### State Evaluation
+
+This subdirectory contains the MCTS evaluation of a given U3T position, given a tensor gamestate. Important details on what the "score" is are described below.
+This streamlines the process of making game evaluation deep learning networks, as the tensor state and output score are given.
+Consider previous explanations to help understand visually how the board looks. It is important to note, though, that if the tensor is printed out, it will
+"look" reflected upon the y-axis from the actual gamestate it represents (each row is correct, but each row is reversed from it's visual representation).
 
+**Features:**
+
+* **`tensor_state` (tensor int32):**
+    * Represents the current game state of the Ultimate Tic-Tac-Toe board.
+    * The tensor is stored as a (4, 9, 9) shape. Each of the 4 layers represents something else, and the (9, 9) part represents the 9 by 9 U3T subgame board.
+      * First layer: X's occupied tiles (1 for X, 0 otherwise)
+      * Second layer: O's occupied tiles (1 for O, 0 otherwise)
+      * Third layer: Legal move positions (1 for valid moves, 0 otherwise)
+      * Fourth layer: Current player's turn (9 by 9 board filled with 1s if X, 0s if O)
+* **`score` (float32):**
+    * Indicates the 'score' of the position (the static evaluation). The score is simply designated as (wins - losses) / num_games. This puts the range of scores between [-1, 1].
+    * It is important to note that unlike a regular evaluation, it is not assumed that -1 is better for O, or 1 is better for X. Rather, a positive score means that a given position is better for the *current player*.
 
 ## Dataset Creation
 
@@ -138,7 +159,7 @@ Stored it as a large parquet file. Then split the data accordingly.
 ### Dataset Splitting
 
 The dataset was split into a train, test, and validation set, with a distribution randomized around the idea of ensuring the correct amount depth in each set 
-(i.e. The train set has 70% of depth 0, 1, 2, ... gamestates, the test has 20%, etc.).
+(i.e. The train set has 70% of depth 0, 1, 2, ... gamestates, the test has 20%, etc.). This applies to all datasets.
 
 * **Training Set:** The training set consists of 70% of the data.
 * **Testing Set:** The test set constains 20% of the data.