combine gtp and network

AlphaGo/game.py

@@ -8,6 +8,7 @@ from __future__ import print_function
 import utils
 import copy
 import tensorflow as tf
+import numpy as np
 from collections import deque
 
 import Network
@@ -185,7 +186,7 @@ class Game:
         self.board = [utils.EMPTY] * (self.size * self.size)
         self.strategy = strategy()
         # self.strategy = None
-        self.executor = Executor(game = self)
+        self.executor = Executor(game=self)
         self.history = []
         self.past = deque(maxlen=8)
         for i in range(8):
@@ -211,8 +212,8 @@ class Game:
     def set_komi(self, k):
         self.komi = k
 
-    def check_valid(self, vertex):
-        return True
+    def check_valid(self, color, vertex):
+        return self.executor.is_valid(color, vertex)
 
     def do_move(self, color, vertex):
         if vertex == utils.PASS:
@@ -224,7 +225,6 @@ class Game:
         # move = self.strategy.gen_move(color)
         # return move
         move = self.strategy.gen_move(self.past, color)
-        print(move)
         self.do_move(color, move)
         return move
 

AlphaGo/self-play.py

@@ -0,0 +1,26 @@
+from game import Game
+from engine import GTPEngine
+
+g = Game()
+
+
+g.show_board()
+e = GTPEngine(game_obj=g)
+
+num = 0
+black_pass = False
+white_pass = False
+while not (black_pass and white_pass):
+    if num % 2 == 0:
+        res=e.run_cmd("genmove BLACK")[0]
+        num += 1
+        print(res)
+        if res == (0,0):
+            black_pass = True
+    else:
+        res = e.run_cmd("genmove WHITE")[0]
+        num += 1
+        print(res)
+        if res == (0, 0):
+            white_pass = True
+    g.show_board()

AlphaGo/strategy.py

@@ -1,18 +1,22 @@
-import os,sys
+import os, sys
+
 sys.path.append(os.path.join(os.path.dirname(__file__), os.path.pardir))
 import numpy as np
 import utils
 import time
+import copy
 import network_small
 import tensorflow as tf
 from collections import deque
 from tianshou.core.mcts.mcts import MCTS
 
+DELTA = [[1, 0], [-1, 0], [0, -1], [0, 1]]
+
 
 class GoEnv:
     def __init__(self, size=9, komi=6.5):
         self.size = size
-        self.komi = 6.5
+        self.komi = komi
         self.board = [utils.EMPTY] * (self.size * self.size)
         self.history = deque(maxlen=8)
 
@@ -102,13 +106,28 @@ class GoEnv:
                     for b in block:
                         self.board[self._flatten(b)] = utils.EMPTY
 
-    def is_valid(self, color, vertex):
+    # def is_valid(self, color, vertex):
+    def is_valid(self, state, action):
+        if action == 81:
+            vertex = (0, 0)
+        else:
+            vertex = (action / 9 + 1, action % 9 + 1)
+        if state[0, 0, 0, -1] == 1:
+            color = 1
+        else:
+            color = -1
+        self.history.clear()
+        for i in range(8):
+            self.history.append((state[:, :, :, i] - state[:, :, :, i + 8]).reshape(-1).tolist())
+        self.board = copy.copy(self.history[-1])
         ### in board
         if not self._in_board(vertex):
             return False
 
         ### already have stone
         if not self.board[self._flatten(vertex)] == utils.EMPTY:
+            # print(np.array(self.board).reshape(9, 9))
+            # print(vertex)
             return False
 
         ### check if it is qi
@@ -127,13 +146,12 @@ class GoEnv:
         id_ = self._flatten(vertex)
         if self.board[id_] == utils.EMPTY:
             self.board[id_] = color
-            self.history.append(self.board)
+            self.history.append(copy.copy(self.board))
             return True
         else:
             return False
 
     def step_forward(self, state, action):
-        # print(state)
         if state[0, 0, 0, -1] == 1:
             color = 1
         else:
@@ -141,7 +159,10 @@ class GoEnv:
         if action == 81:
             vertex = (0, 0)
         else:
-            vertex = (action / 9 + 1, action % 9)
+            vertex = (action % 9 + 1, action / 9 + 1)
+        # print(vertex)
+        # print(self.board)
+        self.board = (state[:, :, :, 7] - state[:, :, :, 15]).reshape(-1).tolist()
         self.do_move(color, vertex)
         new_state = np.concatenate(
             [state[:, :, :, 1:8], (np.array(self.board) == 1).reshape(1, 9, 9, 1),
@@ -162,8 +183,8 @@ class strategy(object):
     def data_process(self, history, color):
         state = np.zeros([1, 9, 9, 17])
         for i in range(8):
-            state[0, :, :, i] = history[i] == 1
-            state[0, :, :, i + 8] = history[i] == -1
+            state[0, :, :, i] = np.array(np.array(history[i]) == np.ones(81)).reshape(9, 9)
+            state[0, :, :, i + 8] = np.array(np.array(history[i]) == -np.ones(81)).reshape(9, 9)
         if color == 1:
             state[0, :, :, 16] = np.ones([9, 9])
         if color == -1:
@@ -171,16 +192,15 @@ class strategy(object):
         return state
 
     def gen_move(self, history, color):
-        self.simulator.history = history
-        self.simulator.board = history[-1]
-        state = self.data_process(history, color)
-        prior = self.evaluator(state)[0]
-        mcts = MCTS(self.simulator, self.evaluator, state, 82, prior, inverse=True, max_step=100)
+        self.simulator.history = copy.copy(history)
+        self.simulator.board = copy.copy(history[-1])
+        state = self.data_process(self.simulator.history, color)
+        mcts = MCTS(self.simulator, self.evaluator, state, 82, inverse=True, max_step=10)
         temp = 1
         p = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
         choice = np.random.choice(82, 1, p=p).tolist()[0]
         if choice == 81:
             move = (0, 0)
         else:
-            move = (choice / 9 + 1, choice % 9 + 1)
+            move = (choice % 9 + 1, choice / 9 + 1)
         return move

AlphaGo/test.py

@@ -1,34 +1,134 @@
-# -*- coding: utf-8 -*-
-# vim:fenc=utf-8
-# $File: test.py
-# $Date: Fri Dec 01 01:3722 2017 +0800
-# $Author: renyong15 © <mails.tsinghua.edu.cn>
-#
-
 from game import Game
 from engine import GTPEngine
 import utils
 
-g = Game()
-e = GTPEngine(game_obj=g)
 
-e.run_cmd("genmove BLACK")
+
+g = Game()
+e = GTPEngine(game_obj = g)
+res = e.run_cmd('1 protocol_version')
+print(e.known_commands)
+print(res)
+
+res = e.run_cmd('2 name')
+print(res)
+
+res = e.run_cmd('3 known_command quit')
+print(res)
+
+res = e.run_cmd('4 unknown_command quitagain')
+print(res)
+
+res = e.run_cmd('5 list_commands')
+print(res)
+
+res = e.run_cmd('6 komi 6')
+print(res)
+
+res = e.run_cmd('7 play BLACK C3')
+print(res)
+
+res = e.run_cmd('play BLACK C4')
+res = e.run_cmd('play BLACK C5')
+res = e.run_cmd('play BLACK C6')
+res = e.run_cmd('play BLACK D3')
+print(res)
+
+res = e.run_cmd('8 genmove BLACK')
+print(res)
+
+#g.show_board()
+print(g.check_valid((10, 9)))
+print(g.executor._neighbor((1,1)))
+print(g.do_move(utils.WHITE, (4, 6)))
+#g.show_board()
+
+
+res = e.run_cmd('play BLACK L10')
+res = e.run_cmd('play BLACK L11')
+res = e.run_cmd('play BLACK L12')
+res = e.run_cmd('play BLACK L13')
+res = e.run_cmd('play BLACK L14')
+res = e.run_cmd('play BLACK m15')
+res = e.run_cmd('play BLACK m9')
+res = e.run_cmd('play BLACK C9')
+res = e.run_cmd('play BLACK D9')
+res = e.run_cmd('play BLACK E9')
+res = e.run_cmd('play BLACK F9')
+res = e.run_cmd('play BLACK G9')
+res = e.run_cmd('play BLACK H9')
+res = e.run_cmd('play BLACK I9')
+
+res = e.run_cmd('play BLACK N9')
+res = e.run_cmd('play BLACK N15')
+res = e.run_cmd('play BLACK O10')
+res = e.run_cmd('play BLACK O11')
+res = e.run_cmd('play BLACK O12')
+res = e.run_cmd('play BLACK O13')
+res = e.run_cmd('play BLACK O14')
+res = e.run_cmd('play BLACK M12')
+
+res = e.run_cmd('play WHITE M10')
+res = e.run_cmd('play WHITE M11')
+res = e.run_cmd('play WHITE N10')
+res = e.run_cmd('play WHITE N11')
+
+res = e.run_cmd('play WHITE M13')
+res = e.run_cmd('play WHITE M14')
+res = e.run_cmd('play WHITE N13')
+res = e.run_cmd('play WHITE N14')
+print(res)
+
+res = e.run_cmd('play BLACK N12')
+print(res)
+#g.show_board()
+
+res = e.run_cmd('play BLACK P16')
+res = e.run_cmd('play BLACK P17')
+res = e.run_cmd('play BLACK P18')
+res = e.run_cmd('play BLACK P19')
+res = e.run_cmd('play BLACK Q16')
+res = e.run_cmd('play BLACK R16')
+res = e.run_cmd('play BLACK S16')
+
+res = e.run_cmd('play WHITE S18')
+res = e.run_cmd('play WHITE S17')
+res = e.run_cmd('play WHITE Q19')
+res = e.run_cmd('play WHITE Q18')
+res = e.run_cmd('play WHITE Q17')
+res = e.run_cmd('play WHITE R18')
+res = e.run_cmd('play WHITE R17')
+res = e.run_cmd('play BLACK S19')
+print(res)
+#g.show_board()
+
+res = e.run_cmd('play WHITE R19')
 g.show_board()
-e.run_cmd("genmove WHITE")
+
+res = e.run_cmd('play BLACK S19')
+print(res)
 g.show_board()
-e.run_cmd("genmove BLACK")
+
+res = e.run_cmd('play BLACK S19')
+print(res)
+
+
+res = e.run_cmd('play BLACK E17')
+res = e.run_cmd('play BLACK F16')
+res = e.run_cmd('play BLACK F18')
+res = e.run_cmd('play BLACK G17')
+res = e.run_cmd('play WHITE G16')
+res = e.run_cmd('play WHITE G18')
+res = e.run_cmd('play WHITE H17')
 g.show_board()
-e.run_cmd("genmove WHITE")
+
+res = e.run_cmd('play WHITE F17')
 g.show_board()
-e.run_cmd("genmove BLACK")
+
+res = e.run_cmd('play BLACK G17')
+print(res)
 g.show_board()
-e.run_cmd("genmove WHITE")
-g.show_board()
-e.run_cmd("genmove BLACK")
-g.show_board()
-e.run_cmd("genmove WHITE")
-g.show_board()
-e.run_cmd("genmove BLACK")
-g.show_board()
-e.run_cmd("genmove WHITE")
+
+res = e.run_cmd('play BLACK G19')
+res = e.run_cmd('play BLACK G17')
 g.show_board()

tianshou/core/mcts/mcts.py

@@ -26,7 +26,7 @@ class MCTSNode(object):
         self.children = {}
         self.state = state
         self.action_num = action_num
-        self.prior = prior
+        self.prior = np.array(prior).reshape(-1)
         self.inverse = inverse
 
     def selection(self, simulator):
@@ -35,6 +35,8 @@ class MCTSNode(object):
     def backpropagation(self, action):
         raise NotImplementedError("Need to implement function backpropagation")
 
+    def valid_mask(self, simulator):
+        pass
 
 class UCTNode(MCTSNode):
     def __init__(self, parent, action, state, action_num, prior, inverse=False):
@@ -45,6 +47,7 @@ class UCTNode(MCTSNode):
         self.ucb = self.Q + c_puct * self.prior * math.sqrt(np.sum(self.N)) / (self.N + 1)
 
     def selection(self, simulator):
+        self.valid_mask(simulator)
         action = np.argmax(self.ucb)
         if action in self.children.keys():
             return self.children[action].selection(simulator)
@@ -66,6 +69,11 @@ class UCTNode(MCTSNode):
             else:
                 self.parent.backpropagation(self.children[action].reward)
 
+    def valid_mask(self, simulator):
+        for act in range(self.action_num - 1):
+            if not simulator.is_valid(self.state, act):
+                self.ucb[act] = -float("Inf")
+
 
 class TSNode(MCTSNode):
     def __init__(self, parent, action, state, action_num, prior, method="Gaussian", inverse=False):
@@ -78,7 +86,7 @@ class TSNode(MCTSNode):
             self.sigma = np.zeros([action_num])
 
 
-class ActionNode:
+class ActionNode(object):
     def __init__(self, parent, action):
         self.parent = parent
         self.action = action
@@ -120,18 +128,19 @@ class ActionNode:
                                                      self.parent.inverse)
             return value
         else:
-            return 0
+            return 0.
 
     def backpropagation(self, value):
         self.reward += value
         self.parent.backpropagation(self.action)
 
 
-class MCTS:
-    def __init__(self, simulator, evaluator, root, action_num, prior, method="UCT", inverse=False, max_step=None,
+class MCTS(object):
+    def __init__(self, simulator, evaluator, root, action_num, method="UCT", inverse=False, max_step=None,
                  max_time=None):
         self.simulator = simulator
         self.evaluator = evaluator
+        prior, _ = self.evaluator(root)
         self.action_num = action_num
         if method == "":
             self.root = root

tianshou/core/mcts/mcts_test.py

@@ -28,7 +28,7 @@ class TestEnv:
             if step == self.max_step:
                 reward = int(np.random.uniform() < self.reward[num])
             else:
-                reward = 0
+                reward = 0.
         return new_state, reward
 
 
@@ -36,4 +36,4 @@ if __name__ == "__main__":
     env = TestEnv(2)
     rollout = rollout_policy(env, 2)
     evaluator = lambda state: rollout(state)
-    mcts = MCTS(env, evaluator, [0, 0], 2, np.array([0.5, 0.5]), max_step=1e4)
+    mcts = MCTS(env, evaluator, [0, 0], 2, max_step=1e4)