diff --git a/src/algorithms/ai/minimax/Minimax.js b/src/algorithms/ai/minimax/Minimax.js
index 46792801..1239e0fd 100644
--- a/src/algorithms/ai/minimax/Minimax.js
+++ b/src/algorithms/ai/minimax/Minimax.js
@@ -31,33 +31,50 @@ class MinimaxPlayer extends Player {
    *
    * @param {GameNode} node - Description of game state
    * @param {Number} depth - Limit to the search depth
-   * @returns {Number} - Best score the player can reach under this state
+   * @returns {[Number, GameNode]} - Best score the player can reach under this
+   * state, and the resulting game state
    */
   minimax(node, depth) {
+    // Check whether search depth is reached
+    // or if the state is terminal
     if (depth === 0 || node.isTerminalState()) {
       return [this.heuristic(node), null];
     }
 
+    // One player is maximizing and the other play is minimizing
+    // Their optimal values are initialized to numbers which will be replaced
+    // as soon as any valid node is visited
     let optimal = node.isOpponentPlaying() ? Number.POSITIVE_INFINITY : Number.NEGATIVE_INFINITY;
     let optimal_node = null
+
+    // Make a list of possible next states
     const nextNodes = node.computeNextStates();
 
+    // Iterate over all possible next states
+    // Choose the optimal score, from the perspective of current player
     for (let i = 0; i < nextNodes.length; i += 1) {
       const nextNode = nextNodes[i];
+
+      // Recursively call minimax to find score of the next state
       const [score, _] = this.minimax(nextNode, depth - 1);
 
+      // In this code, the "opponent" is the minimizing player
+      // And the "player" is the maximizing player
       if (node.isOpponentPlaying()) {
+        // optimal is set to min(score, optimal)
         if (score < optimal) {
           optimal = score;
           optimal_node = nextNode;
         }
       } else {
+        // optimal is set to max(score, optimal)
         if (score > optimal) {
           optimal = score;
           optimal_node = nextNode;
         }
       }
     }
+    // Return optimal score and resulting game state
     return [optimal, optimal_node];
   }
 }