feat: modules for significant sections

shogi-ai/agent.py → shogi-ai/agents/agent.py

@@ -2,7 +2,7 @@
 Agent class is the base class for all agents.
 """
 
-from environment import Environment
+from env.environment import Environment
 from shogi import Board
 
 

shogi-ai/mcts_agent.py → shogi-ai/agents/mcts_agent.py

@@ -1,30 +1,41 @@
+"""
+
+A monte-carlo tree search for playing shogi
+
+"""
 
-import time
-import random
 import math
+import random
+import time
+from typing import List, Optional
 
-from shogi import Board
-from shogi import Move
-from environment import Environment
-from agent import Agent
-from typing import Optional
+from agents.agent import Agent
+from env.environment import Environment
+from shogi import Board, Move
 
 
-class Node:
+class Node:  # pylint: disable=too-few-public-methods
+    """
+    Node class for moves in the MCTS tree.
+    """
+
     # we are at first going to have a max depth of recorded
     # nodes of 1. This might be a bad idea, but we will see
 
     def __init__(self, move: Optional[Move], parent=None):
         self.move = move
         self.parent = parent
-        self.children = []
+        self.children: List[Node] = []
         self.visits = 0
         self.value = 0
 
         if parent:
             parent.children.append(self)
 
     def get_child_from_move(self, move: Move):
+        """
+        Fetch a child move if it exists for the given move.
+        """
         for child in self.children:
             if child.move == move:
                 return child
@@ -92,14 +103,19 @@ def _selection(self) -> Node:
             curr_node_visits = max(1, current_node.visits)
             tree_visits = max(1, self.tree.visits)
 
-            uct_ucb1 = current_node.value / curr_node_visits + self.exploration_coefficient * math.sqrt(
-                math.log(tree_visits) / curr_node_visits
+            uct_ucb1 = (
+                current_node.value / curr_node_visits
+                + self.exploration_coefficient
+                * math.sqrt(math.log(tree_visits) / curr_node_visits)
             )
             if uct_ucb1 > max_uct_ucb1:
                 max_uct_ucb1 = uct_ucb1
                 node_to_rollout = current_node
             queue.extend(current_node.children)
 
+        if node_to_rollout is None:
+            raise ValueError("No node to rollout")
+
         return node_to_rollout
 
     def _simulation(self, node_to_rollout: Node):
@@ -136,8 +152,7 @@ def _utility(self, board_copy: Board):
         if board_copy.is_checkmate():
             if board_copy.turn != self.player:
                 return 1
-            else:
-                return -1
+            return -1
         return 0
 
     def _backpropagation(self, leaf_node: Node, value: int):
@@ -150,7 +165,7 @@ def _backpropagation(self, leaf_node: Node, value: int):
 
     def _random_move(self, board: Board) -> Move:
         self.positions_checked += 1
-        moves = [move for move in board.pseudo_legal_moves]
+        moves = list(board.pseudo_legal_moves)
         move = None
         while True:
             move = random.choice(moves)
@@ -161,7 +176,7 @@ def _random_move(self, board: Board) -> Move:
 
     def _expansion(self, board: Board, parent_node: Node) -> None:
         move = self._random_move(board)
-        node = Node(move=move, parent=parent_node)
+        Node(move=move, parent=parent_node)
 
     @classmethod
     def from_board(cls, board: Board):

shogi-ai/random_agent.py → shogi-ai/agents/random_agent.py

@@ -7,11 +7,10 @@
 
 import random
 
-from agent import Agent
-from environment import Environment
+from agents.agent import Agent
+from env.environment import Environment
 from shogi import Board
 from shogi.Move import Move
-from typing import Optional
 
 
 class RandomAgent(Agent):
@@ -33,7 +32,7 @@ def __init__(self, env: Environment, player: int, strategy=None):
         strategy = "random"
         super().__init__(env=env, player=player, strategy=strategy)
 
-    def select_action(self, board: Optional[Board] = None) -> Move:
+    def select_action(self) -> Move:
         if self.player != self.env.board.turn:
             raise ValueError("Not the player's turn")
         legal_moves = self._env.action_space

shogi-ai/evaluation/grimbergen.py

@@ -0,0 +1,38 @@
+"""
+
+This contains the mapping used for
+the Grimbergen evaluation of pieces.
+
+"""
+
+from shogi import (
+    BISHOP,
+    GOLD,
+    KNIGHT,
+    LANCE,
+    PAWN,
+    PROM_BISHOP,
+    PROM_KNIGHT,
+    PROM_LANCE,
+    PROM_PAWN,
+    PROM_ROOK,
+    PROM_SILVER,
+    ROOK,
+    SILVER,
+)
+
+GRIMBERGEN = {
+    PAWN: 1,
+    LANCE: 3,
+    KNIGHT: 3,
+    SILVER: 5,
+    GOLD: 5,
+    BISHOP: 8,
+    ROOK: 9,
+    PROM_PAWN: 5,
+    PROM_LANCE: 5,
+    PROM_KNIGHT: 5,
+    PROM_SILVER: 5,
+    PROM_BISHOP: 12,
+    PROM_ROOK: 13,
+}

shogi-ai/main.py

@@ -9,14 +9,11 @@
 """
 
 import shogi
-import sys
-from environment import Environment
-from random_agent import RandomAgent
-from mcts_agent import MctsAgent
+from agents.mcts_agent import MctsAgent
+from agents.random_agent import RandomAgent
+from env.environment import Environment
 from shogi import Board, Move
 
-sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf-8", buffering=1)
-
 
 def main() -> None:
     """
@@ -29,7 +26,6 @@ def main() -> None:
 
     agent2: RandomAgent = RandomAgent(env, player=1)
 
-    agent1_action: Move = agent1.select_action()
     while not board.is_game_over():
         agent1_action: Move = agent1.select_action()
         board.push(agent1_action)
@@ -50,7 +46,7 @@ def main() -> None:
     print(f"Simulated games: {agent1.games_simulated}")
     print(f"Rollouts: {agent1.rollouts}")
     print(f"Positions Checked: {agent1.positions_checked}")
-    with open("game.txt", "w") as f:
+    with open("game.txt", "w", encoding="utf-8") as f:
         for move in board.move_stack:
             f.write(str(move) + "\n")