From fc8114fe35646673e4b2f4ac00527879878a6ce3 Mon Sep 17 00:00:00 2001 From: Dong Yan Date: Tue, 19 Dec 2017 16:51:50 +0800 Subject: [PATCH] merge flatten and deflatten, rename variable for clarity --- AlphaGo/engine.py | 4 +-- AlphaGo/game.py | 15 ++++++----- AlphaGo/strategy.py | 45 +++++++++++++-------------------- tianshou/core/mcts/evaluator.py | 4 +-- tianshou/core/mcts/mcts.py | 2 +- 5 files changed, 31 insertions(+), 39 deletions(-) diff --git a/AlphaGo/engine.py b/AlphaGo/engine.py index 1f9af85..1ee8833 100644 --- a/AlphaGo/engine.py +++ b/AlphaGo/engine.py @@ -167,7 +167,7 @@ class GTPEngine(): move = self._parse_move(args) if move: color, vertex = move - res = self._game.do_move(color, vertex) + res = self._game.play_move(color, vertex) if res: return None, True else: @@ -177,7 +177,7 @@ class GTPEngine(): def cmd_genmove(self, args, **kwargs): color = self._parse_color(args) if color: - move = self._game.gen_move(color) + move = self._game.think_play_move(color) return self._vertex_point2string(move), True else: return 'unknown player', False diff --git a/AlphaGo/game.py b/AlphaGo/game.py index 2a82d8e..d0cb91c 100644 --- a/AlphaGo/game.py +++ b/AlphaGo/game.py @@ -77,7 +77,7 @@ class Game: state[0, :, :, 16] = np.zeros([self.size, self.size]) return state - def strategy_gen_move(self, latest_boards, color): + def think(self, latest_boards, color): self.simulator.simulate_latest_boards = copy.copy(latest_boards) self.simulator.simulate_board = copy.copy(latest_boards[-1]) nn_input = self.generate_nn_input(self.simulator.simulate_latest_boards, color) @@ -91,17 +91,18 @@ class Game: move = self._deflatten(choice) return move, prob - def do_move(self, color, vertex): + def play_move(self, color, vertex): + # this function can be called directly to play the opponent's move if vertex == utils.PASS: return True res = self.executor.do_move(color, vertex) return res - def gen_move(self, color): - # move = self.strategy.gen_move(color) - # return move - move, self.prob = self.strategy_gen_move(self.latest_boards, color) - self.do_move(color, move) + def think_play_move(self, color): + # although we dont need to return self.prob, however it is needed for neural network training + move, self.prob = self.think(self.latest_boards, color) + # play the move immediately + self.play_move(color, move) return move def status2symbol(self, s): diff --git a/AlphaGo/strategy.py b/AlphaGo/strategy.py index 112f130..af017b1 100644 --- a/AlphaGo/strategy.py +++ b/AlphaGo/strategy.py @@ -10,7 +10,7 @@ import tensorflow as tf from collections import deque from tianshou.core.mcts.mcts import MCTS -DELTA = [[1, 0], [-1, 0], [0, -1], [0, 1]] +NEIGHBOR_OFFSET = [[1, 0], [-1, 0], [0, -1], [0, 1]] CORNER_OFFSET = [[-1, -1], [-1, 1], [1, 1], [1, -1]] class GoEnv: @@ -19,17 +19,8 @@ class GoEnv: self.simulate_board = [utils.EMPTY] * (self.game.size ** 2) self.simulate_latest_boards = deque(maxlen=8) - def simulate_flatten(self, vertex): - x, y = vertex - return (x - 1) * self.game.size + (y - 1) - - def simulate_deflatten(self, idx): - x = idx // self.game.size + 1 - y = idx % self.game.size + 1 - return (x, y) - def _find_group(self, start): - color = self.simulate_board[self.simulate_flatten(start)] + color = self.simulate_board[self.game._flatten(start)] # print ("color : ", color) chain = set() frontier = [start] @@ -40,32 +31,32 @@ class GoEnv: chain.add(current) for n in self._neighbor(current): # print n, self._flatten(n), self.board[self._flatten(n)], - if self.simulate_board[self.simulate_flatten(n)] == color and not n in chain: + if self.simulate_board[self.game._flatten(n)] == color and not n in chain: frontier.append(n) - if self.simulate_board[self.simulate_flatten(n)] == utils.EMPTY: + if self.simulate_board[self.game._flatten(n)] == utils.EMPTY: has_liberty = True return has_liberty, chain def _is_suicide(self, color, vertex): - self.simulate_board[self.simulate_flatten(vertex)] = color # assume that we already take this move + self.simulate_board[self.game._flatten(vertex)] = color # assume that we already take this move suicide = False has_liberty, group = self._find_group(vertex) if not has_liberty: suicide = True # no liberty, suicide for n in self._neighbor(vertex): - if self.simulate_board[self.simulate_flatten(n)] == utils.another_color(color): + if self.simulate_board[self.game._flatten(n)] == utils.another_color(color): opponent_liberty, group = self._find_group(n) if not opponent_liberty: suicide = False # this move is able to take opponent's stone, not suicide - self.simulate_board[self.simulate_flatten(vertex)] = utils.EMPTY # undo this move + self.simulate_board[self.game._flatten(vertex)] = utils.EMPTY # undo this move return suicide def _check_global_isomorphous(self, color, vertex): ##backup _board = copy.copy(self.simulate_board) - self.simulate_board[self.simulate_flatten(vertex)] = color + self.simulate_board[self.game._flatten(vertex)] = color self._process_board(color, vertex) if self.simulate_board in self.game.history: res = True @@ -84,7 +75,7 @@ class GoEnv: def _neighbor(self, vertex): x, y = vertex nei = [] - for d in DELTA: + for d in NEIGHBOR_OFFSET: _x = x + d[0] _y = y + d[1] if self._in_board((_x, _y)): @@ -104,16 +95,16 @@ class GoEnv: def _process_board(self, color, vertex): nei = self._neighbor(vertex) for n in nei: - if self.simulate_board[self.simulate_flatten(n)] == utils.another_color(color): + if self.simulate_board[self.game._flatten(n)] == utils.another_color(color): has_liberty, group = self._find_group(n) if not has_liberty: for b in group: - self.simulate_board[self.simulate_flatten(b)] = utils.EMPTY + self.simulate_board[self.game._flatten(b)] = utils.EMPTY def _is_eye(self, color, vertex): nei = self._neighbor(vertex) cor = self._corner(vertex) - ncolor = {color == self.simulate_board[self.simulate_flatten(n)] for n in nei} + ncolor = {color == self.simulate_board[self.game._flatten(n)] for n in nei} if False in ncolor: # print "not all neighbors are in same color with us" return False @@ -122,7 +113,7 @@ class GoEnv: # print "all neighbors are in same group and same color with us" return True else: - opponent_number = [self.simulate_board[self.simulate_flatten(c)] for c in cor].count(-color) + opponent_number = [self.simulate_board[self.game._flatten(c)] for c in cor].count(-color) opponent_propotion = float(opponent_number) / float(len(cor)) if opponent_propotion < 0.5: # print "few opponents, real eye" @@ -145,7 +136,7 @@ class GoEnv: if action == self.game.size ** 2: vertex = (0, 0) else: - vertex = self.simulate_deflatten(action) + vertex = self.game._deflatten(action) if state[0, 0, 0, -1] == utils.BLACK: color = utils.BLACK else: @@ -160,7 +151,7 @@ class GoEnv: return False ### already have stone - if not self.simulate_board[self.simulate_flatten(vertex)] == utils.EMPTY: + if not self.simulate_board[self.game._flatten(vertex)] == utils.EMPTY: # print(np.array(self.board).reshape(9, 9)) # print(vertex) return False @@ -182,14 +173,14 @@ class GoEnv: if vertex == utils.PASS: return True - id_ = self.simulate_flatten(vertex) + id_ = self.game._flatten(vertex) if self.simulate_board[id_] == utils.EMPTY: self.simulate_board[id_] = color return True else: return False - def step_forward(self, state, action): + def simulate_step_forward(self, state, action): if state[0, 0, 0, -1] == 1: color = utils.BLACK else: @@ -197,7 +188,7 @@ class GoEnv: if action == self.game.size ** 2: vertex = utils.PASS else: - vertex = self.simulate_deflatten(action) + vertex = self.game._deflatten(action) # print(vertex) # print(self.board) self.simulate_board = (state[:, :, :, 7] - state[:, :, :, 15]).reshape(-1).tolist() diff --git a/tianshou/core/mcts/evaluator.py b/tianshou/core/mcts/evaluator.py index 9c4ee8e..a1f9456 100644 --- a/tianshou/core/mcts/evaluator.py +++ b/tianshou/core/mcts/evaluator.py @@ -19,10 +19,10 @@ class rollout_policy(evaluator): # TODO: prior for rollout policy total_reward = 0. action = np.random.randint(0, self.action_num) - state, reward = self.env.step_forward(state, action) + state, reward = self.env.simulate_step_forward(state, action) total_reward += reward while state is not None: action = np.random.randint(0, self.action_num) - state, reward = self.env.step_forward(state, action) + state, reward = self.env.simulate_step_forward(state, action) total_reward += reward return np.ones([self.action_num])/self.action_num, total_reward diff --git a/tianshou/core/mcts/mcts.py b/tianshou/core/mcts/mcts.py index 979e994..b58c105 100644 --- a/tianshou/core/mcts/mcts.py +++ b/tianshou/core/mcts/mcts.py @@ -116,7 +116,7 @@ class ActionNode(object): self.next_state = tuple2list(self.next_state) def selection(self, simulator): - self.next_state, self.reward = simulator.step_forward(self.parent.state, self.action) + self.next_state, self.reward = simulator.simulate_step_forward(self.parent.state, self.action) self.origin_state = self.next_state self.state_type = type(self.next_state) self.type_conversion_to_tuple()