Merge branch 'experimentation' into 'master'
Experimentation See merge request Pownie/backgammon!6
This commit is contained in:
commit
c54f7aca24
135
app.py
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135
app.py
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@ -0,0 +1,135 @@
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from flask import Flask, request, jsonify
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from flask_json import FlaskJSON, as_json_p
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from flask_cors import CORS
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from board import Board
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import main
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import random
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from network import Network
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app = Flask(__name__)
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app.config['JSON_ADD_STATUS'] = False
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app.config['JSON_JSONP_OPTIONAL'] = False
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json = FlaskJSON(app)
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CORS(app)
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config = main.config.copy()
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config['model'] = "player_testings"
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config['ply'] = "1"
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config['board_representation'] = 'quack-fat'
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network = Network(config, config['model'])
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network.restore_model()
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def calc_move_sets(from_board, roll, player):
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board = from_board
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sets = []
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total = 0
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for r in roll:
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# print("Value of r:", r)
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sets.append([Board.calculate_legal_states(board, player, [r, 0]), r])
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total += r
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sets.append([Board.calculate_legal_states(board, player, roll), total])
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return sets
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def tmp_name(from_board, to_board, roll, player, total_moves, is_quad=False):
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sets = calc_move_sets(from_board, roll, player)
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return_board = from_board
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print("To board:\n",to_board)
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print("All sets:\n",sets)
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for idx, board_set in enumerate(sets):
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board_set[0] = list(board_set[0])
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# print(to_board)
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# print(board_set)
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if to_board in board_set[0]:
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# print("To board:", to_board)
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# print(board_set[0])
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# print(board_set[1])
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total_moves -= board_set[1]
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# if it's not the sum of the moves
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if idx < (4 if is_quad else 2):
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roll[idx] = 0
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else:
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roll = [0, 0]
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return_board = to_board
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break
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# print("Return board!:\n",return_board)
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return total_moves, roll, return_board
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def calc_move_stuff(from_board, to_board, roll, player, total_roll, is_quad):
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total_moves, roll, board = tmp_name(from_board, to_board, list(roll), player, total_roll, is_quad)
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return board, total_moves, roll
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@app.route('/get_board', methods=['GET'])
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@as_json_p
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def get_board():
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return {'board':'0, 2, 0, 0, 0, 0, -5, 0, -3, 0, 0, 0, 5, -5, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, -2, 0'}
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def check_move(prev, curr):
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# TODO: Decide on player system and implement roll properly
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legal_states = Board.calculate_legal_states(tuple(prev), -1, [1,2])
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truth_list = [list(curr) == list(ele) for ele in legal_states]
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return any(truth_list)
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@app.route('/bot_move', methods=['POST'])
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def bot_move():
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data = request.get_json(force=True)
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board = [int(x) for x in data['board'].split(',')]
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roll = (random.randrange(1,7), random.randrange(1,7))
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# print(roll)
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board, _ = network.make_move(tuple(board), roll, 1)
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# print("Boards!:",board)
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return ",".join([str(x) for x in list(board)])
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@app.route('/post_board', methods=['POST'])
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def post_board():
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data = request.get_json(force=True)
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# TODO: Fix hardcoded player
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player = -1
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board = [int(x) for x in data['board'].split(',')]
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prev_board = [int(x) for x in data['prevBoard'].split(',')]
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print(data['roll'])
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roll = [int(x) for x in data['roll'].split(',')]
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print(roll)
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quad = data['quad'] == "true"
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# print(board)
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total_roll = int(data['totalRoll'])
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print("total roll is:", total_roll)
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return_board, total_moves, roll = calc_move_stuff(tuple(prev_board), tuple(board), tuple(roll), player, total_roll, quad)
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str_board = ",".join([str(x) for x in return_board])
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str_roll = ",".join([str(x) for x in roll])
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return_string = str_board + "#" + str(total_moves) + "#" + str_roll
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print(return_string)
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return return_string
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if __name__ == '__main__':
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app.run(host = '0.0.0.0', port=35270)
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78
bin/0-ply-tests.rb
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78
bin/0-ply-tests.rb
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@ -0,0 +1,78 @@
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def run_stuff(board_rep, model_name, ply)
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epi_count = 0
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system("python3 main.py --train --model #{model_name} --board-rep #{board_rep} --episodes 1 --ply #{ply}")
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while epi_count < 200000 do
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system("python3 main.py --eval --model #{model_name} --eval-methods dumbeval --episodes 250 --ply #{ply} --repeat-eval 3")
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system("python3 main.py --eval --model #{model_name} --eval-methods pubeval --episodes 250 --ply #{ply} --repeat-eval 3")
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system("python3 main.py --train --model #{model_name} --episodes 2000 --ply #{ply}")
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epi_count += 2000
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end
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end
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### ///////////////////////////////////////////////////////////////
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# QUACK TESTINGS
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### ///////////////////////////////////////////////////////////////
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board_rep = "quack"
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model_name = "quack_test_0_ply"
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ply = 0
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run_stuff(board_rep, model_name, ply)
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# board_rep = "quack"
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# model_name = "quack_test_1_ply"
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# ply = 1
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# run_stuff(board_rep, model_name, ply)
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### ///////////////////////////////////////////////////////////////
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# QUACK-FAT TESTING
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### ///////////////////////////////////////////////////////////////
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board_rep = "quack-fat"
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model_name = "quack-fat_test_0_ply"
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ply = 0
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run_stuff(board_rep, model_name, ply)
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# board_rep = "quack-fat"
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# model_name = "quack-fat_test_1_ply"
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# ply = 1
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# run_stuff(board_rep, model_name, ply)
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### ///////////////////////////////////////////////////////////////
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# QUACK-NORM TESTING
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### ///////////////////////////////////////////////////////////////
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board_rep = "quack-norm"
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model_name = "quack-norm_test_0_ply"
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ply = 1
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run_stuff(board_rep, model_name, ply)
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# board_rep = "quack-norm"
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# model_name = "quack-norm_test_1_ply"
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# ply = 1
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# run_stuff(board_rep, model_name, ply)
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### ///////////////////////////////////////////////////////////////
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# TESAURO TESTING
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### ///////////////////////////////////////////////////////////////
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board_rep = "tesauro"
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model_name = "tesauro_test_0_ply"
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ply = 1
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run_stuff(board_rep, model_name, ply)
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# board_rep = "tesauro"
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# model_name = "tesauro_test_1_ply"
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# ply = 1
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# run_stuff(board_rep, model_name, ply)
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10
main.py
10
main.py
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@ -77,15 +77,6 @@ if not os.path.isdir(model_path()):
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if not os.path.isdir(log_path):
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os.mkdir(log_path)
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def save_config():
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import yaml
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# checkpoint_path = os.path.join(config['model_storage_path'], config['model'])
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# config_path = os.path.join(checkpoint_path, 'config')
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# with open(config_path, 'a+') as f:
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# print("lol")
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print(yaml.dump(config))
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# Define helper functions
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def log_train_outcome(outcome, diff_in_values, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
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format_vars = { 'trained_eps': trained_eps,
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@ -172,7 +163,6 @@ if __name__ == "__main__":
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# Set up network
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from network import Network
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save_config()
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# Set up variables
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episode_count = config['episode_count']
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184
network.py
184
network.py
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@ -93,7 +93,7 @@ class Network:
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:param decay_steps: The amount of steps between each decay
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:return: The result of the exponential decay performed on the learning rate
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"""
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res = max_lr * decay_rate**(global_step // decay_steps)
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res = max_lr * decay_rate ** (global_step // decay_steps)
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return res
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def do_backprop(self, prev_state, value_next):
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@ -104,8 +104,8 @@ class Network:
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:return: Nothing, the calculation is performed on the model of the network
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"""
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self.learning_rate = tf.maximum(self.min_learning_rate,
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self.exp_decay(self.max_learning_rate, self.global_step, 0.96, 50000),
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name="learning_rate")
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self.exp_decay(self.max_learning_rate, self.global_step, 0.96, 50000),
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name="learning_rate")
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with tf.GradientTape() as tape:
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value = self.model(prev_state.reshape(1,-1))
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@ -165,8 +165,7 @@ class Network:
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:param states: A number of states. The states have to be transformed before being given to this function.
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:return:
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"""
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values = self.model.predict_on_batch(states)
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return values
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return self.model.predict_on_batch(states)
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def restore_model(self):
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@ -174,7 +173,6 @@ class Network:
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Restore a model for a session, such that a trained model and either be further trained or
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used for evaluation
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:param sess: Current session
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:return: Nothing. It's a side-effect that a model gets restored for the network.
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"""
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@ -211,7 +209,6 @@ class Network:
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and then picking the best, by using the network to evaluate each state. This is 0-ply, ie. no look-ahead.
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The highest score is picked for the 1-player and the max(1-score) is picked for the -1-player.
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:param sess:
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:param board: Current board
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:param roll: Current roll
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:param player: Current player
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@ -224,10 +221,9 @@ class Network:
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transformed_scores = [x if np.sign(player) > 0 else 1 - x for x in scores]
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best_score_idx = np.argmax(np.array(transformed_scores))
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best_move = legal_moves[best_score_idx]
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best_score = scores[best_score_idx]
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best_move, best_score = legal_moves[best_score_idx], scores[best_score_idx]
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return [best_move, best_score]
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return (best_move, best_score)
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def make_move_1_ply(self, board, roll, player):
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"""
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@ -237,9 +233,9 @@ class Network:
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:param player:
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:return:
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"""
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# start = time.time()
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start = time.time()
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best_pair = self.calculate_1_ply(board, roll, player)
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# print(time.time() - start)
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#print(time.time() - start)
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return best_pair
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@ -248,35 +244,30 @@ class Network:
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Find the best move based on a 1-ply look-ahead. First the x best moves are picked from a 0-ply and then
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all moves and scores are found for them. The expected score is then calculated for each of the boards from the
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0-ply.
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:param sess:
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:param board:
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:param roll: The original roll
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:param player: The current player
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:return: Best possible move based on 1-ply look-ahead
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"""
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# find all legal states from the given board and the given roll
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init_legal_states = Board.calculate_legal_states(board, player, roll)
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legal_states = np.array([self.board_trans_func(state, player)[0] for state in init_legal_states])
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scores = self.calc_vals(legal_states)
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scores = [score.numpy() for score in scores]
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scores = [ score.numpy()
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for score
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in self.calc_vals(legal_states) ]
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moves_and_scores = list(zip(init_legal_states, scores))
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sorted_moves_and_scores = sorted(moves_and_scores, key=itemgetter(1), reverse=player==1)
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|
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best_boards = [x[0] for x in sorted_moves_and_scores[:10]]
|
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|
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|
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sorted_moves_and_scores = sorted(moves_and_scores, key=itemgetter(1), reverse=(player == 1))
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best_boards = [ x[0] for x in sorted_moves_and_scores[:10] ]
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scores, trans_scores = self.do_ply(best_boards, player)
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best_score_idx = np.array(trans_scores).argmax()
|
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|
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return [best_boards[best_score_idx], scores[best_score_idx]]
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return (best_boards[best_score_idx], scores[best_score_idx])
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def do_ply(self, boards, player):
|
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"""
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|
|
@ -285,7 +276,6 @@ class Network:
|
|||
allowing the function to search deeper, which could result in an even larger search space. If we wish
|
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to have more than 2-ply, this should be fixed, so we could extend this method to allow for 3-ply.
|
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|
||||
:param sess:
|
||||
:param boards: The boards to try all rolls on
|
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:param player: The player of the previous ply
|
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:return: An array of scores where each index describes one of the boards which was given as param
|
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|
|
@ -305,11 +295,11 @@ class Network:
|
|||
length_list = []
|
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test_list = []
|
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# Prepping of data
|
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start= time.time()
|
||||
start = time.time()
|
||||
for board in boards:
|
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length = 0
|
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for roll in all_rolls:
|
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all_states = list(Board.calculate_legal_states(board, player*-1, roll))
|
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all_states = Board.calculate_legal_states(board, player*-1, roll)
|
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for state in all_states:
|
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state = np.array(self.board_trans_func(state, player*-1)[0])
|
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test_list.append(state)
|
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|
|
@ -318,148 +308,21 @@ class Network:
|
|||
|
||||
# print(time.time() - start)
|
||||
|
||||
start = time.time()
|
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# start = time.time()
|
||||
|
||||
all_scores_legit = self.model.predict_on_batch(np.array(test_list))
|
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all_scores = self.model.predict_on_batch(np.array(test_list))
|
||||
|
||||
split_scores = []
|
||||
from_idx = 0
|
||||
for length in length_list:
|
||||
split_scores.append(all_scores_legit[from_idx:from_idx+length])
|
||||
split_scores.append(all_scores[from_idx:from_idx+length])
|
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from_idx += length
|
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|
||||
means_splits = [tf.reduce_mean(scores) for scores in split_scores]
|
||||
transformed_means_splits = [x if player == 1 else (1-x) for x in means_splits]
|
||||
# print(time.time() - start)
|
||||
|
||||
return ([means_splits, transformed_means_splits])
|
||||
|
||||
|
||||
def calc_n_ply(self, n_init, sess, board, player, roll):
|
||||
"""
|
||||
:param n_init:
|
||||
:param sess:
|
||||
:param board:
|
||||
:param player:
|
||||
:param roll:
|
||||
:return:
|
||||
"""
|
||||
|
||||
# find all legal states from the given board and the given roll
|
||||
init_legal_states = Board.calculate_legal_states(board, player, roll)
|
||||
|
||||
# find all values for the above boards
|
||||
zero_ply_moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in init_legal_states]
|
||||
|
||||
# pythons reverse is in place and I can't call [:15] on it, without applying it to an object like so. Fuck.
|
||||
sorted_moves_and_scores = sorted(zero_ply_moves_and_scores, key=itemgetter(1), reverse=player==1)
|
||||
|
||||
|
||||
best_boards = [x[0] for x in sorted_moves_and_scores[:10]]
|
||||
|
||||
best_move_score_pair = self.n_ply(n_init, sess, best_boards, player)
|
||||
|
||||
return best_move_score_pair
|
||||
|
||||
|
||||
def n_ply(self, n_init, sess, boards_init, player_init):
|
||||
"""
|
||||
:param n_init:
|
||||
:param sess:
|
||||
:param boards_init:
|
||||
:param player_init:
|
||||
:return:
|
||||
"""
|
||||
def ply(n, boards, player):
|
||||
def calculate_possible_states(board):
|
||||
possible_rolls = [ (1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
|
||||
(1, 6), (2, 2), (2, 3), (2, 4), (2, 5),
|
||||
(2, 6), (3, 3), (3, 4), (3, 5), (3, 6),
|
||||
(4, 4), (4, 5), (4, 6), (5, 5), (5, 6),
|
||||
(6, 6) ]
|
||||
|
||||
# for roll in possible_rolls:
|
||||
# print(len(Board.calculate_legal_states(board, player, roll)))
|
||||
|
||||
return [ Board.calculate_legal_states(board, player, roll)
|
||||
for roll
|
||||
in possible_rolls ]
|
||||
|
||||
def find_best_state_score(boards):
|
||||
score_pairs = [ (board, self.eval_state(sess, self.board_trans_func(board, player)))
|
||||
for board
|
||||
in boards ]
|
||||
scores = [ pair[1]
|
||||
for pair
|
||||
in score_pairs ]
|
||||
best_score_pair = score_pairs[np.array(scores).argmax()]
|
||||
|
||||
return best_score_pair
|
||||
|
||||
def average_score(boards):
|
||||
return sum(boards)/len(boards)
|
||||
|
||||
def average_ply_score(board):
|
||||
states_for_rolls = calculate_possible_states(board)
|
||||
|
||||
best_state_score_for_each_roll = [
|
||||
find_best_state_score(states)
|
||||
for states
|
||||
in states_for_rolls ]
|
||||
best_score_for_each_roll = [ x[1]
|
||||
for x
|
||||
in best_state_score_for_each_roll ]
|
||||
|
||||
average_score_var = average_score(best_score_for_each_roll)
|
||||
return average_score_var
|
||||
|
||||
|
||||
if n == 1:
|
||||
average_score_pairs = [ (board, average_ply_score(board))
|
||||
for board
|
||||
in boards ]
|
||||
return average_score_pairs
|
||||
elif n > 1: # n != 1
|
||||
def average_for_score_pairs(score_pairs):
|
||||
scores = [ pair[1]
|
||||
for pair
|
||||
in score_pairs ]
|
||||
return sum(scores)/len(scores)
|
||||
|
||||
def average_plain(scores):
|
||||
return sum(scores)/len(scores)
|
||||
|
||||
print("+"*20)
|
||||
print(n)
|
||||
print(type(boards))
|
||||
print(boards)
|
||||
possible_states_for_boards = [
|
||||
(board, calculate_possible_states(board))
|
||||
for board
|
||||
in boards ]
|
||||
|
||||
average_score_pairs = [
|
||||
(inner_boards[0], average_plain([ average_for_score_pairs(ply(n - 1, inner_board, player * -1 if n == 1 else player))
|
||||
for inner_board
|
||||
in inner_boards[1] ]))
|
||||
for inner_boards
|
||||
in possible_states_for_boards ]
|
||||
|
||||
return average_score_pairs
|
||||
|
||||
else:
|
||||
assert False
|
||||
|
||||
if n_init < 1: print("Unexpected argument n = {}".format(n_init)); exit()
|
||||
|
||||
boards_with_scores = ply(n_init, boards_init, -1 * player_init)
|
||||
#print("Boards with scores:",boards_with_scores)
|
||||
scores = [ ( pair[1] if player_init == 1 else (1 - pair[1]) )
|
||||
for pair
|
||||
in boards_with_scores ]
|
||||
#print("All the scores:",scores)
|
||||
best_score_pair = boards_with_scores[np.array(scores).argmax()]
|
||||
return best_score_pair
|
||||
return (means_splits, transformed_means_splits)
|
||||
|
||||
|
||||
def eval(self, episode_count, trained_eps = 0):
|
||||
|
|
@ -477,7 +340,6 @@ class Network:
|
|||
"""
|
||||
Do the actual evaluation
|
||||
|
||||
:param sess:
|
||||
:param method: Either pubeval or dumbeval
|
||||
:param episodes: Amount of episodes to use in the evaluation
|
||||
:param trained_eps:
|
||||
|
|
@ -509,11 +371,9 @@ class Network:
|
|||
board = Board.initial_state
|
||||
while Board.outcome(board) is None:
|
||||
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
||||
|
||||
board = (self.make_move(board, roll, 1))[0]
|
||||
|
||||
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
||||
|
||||
board = Eval.make_pubeval_move(board, -1, roll)[0][0:26]
|
||||
|
||||
sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
|
||||
|
|
@ -532,11 +392,9 @@ class Network:
|
|||
board = Board.initial_state
|
||||
while Board.outcome(board) is None:
|
||||
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
||||
|
||||
board = (self.make_move(board, roll, 1))[0]
|
||||
|
||||
roll = (random.randrange(1, 7), random.randrange(1, 7))
|
||||
|
||||
board = Eval.make_dumbeval_move(board, -1, roll)[0][0:26]
|
||||
|
||||
sys.stderr.write("\t outcome {}".format(Board.outcome(board)[1]))
|
||||
|
|
|
|||
18
player.py
18
player.py
|
|
@ -20,21 +20,22 @@ class Player:
|
|||
sets.append([Board.calculate_legal_states(board, player, [r,0]), r])
|
||||
total += r
|
||||
sets.append([Board.calculate_legal_states(board, player, [total,0]), total])
|
||||
print(sets)
|
||||
return sets
|
||||
|
||||
|
||||
def tmp_name(self, from_board, to_board, roll, player, total_moves):
|
||||
def tmp_name(self, from_board, to_board, roll, player, total_moves, is_quad = False):
|
||||
sets = self.calc_move_sets(from_board, roll, player)
|
||||
return_board = from_board
|
||||
for idx, board_set in enumerate(sets):
|
||||
|
||||
board_set[0] = list(board_set[0])
|
||||
print(to_board)
|
||||
print(board_set)
|
||||
# print(to_board)
|
||||
# print(board_set)
|
||||
if to_board in board_set[0]:
|
||||
total_moves -= board_set[1]
|
||||
# if it's not the sum of the moves
|
||||
if idx < 2:
|
||||
if idx < (4 if is_quad else 2):
|
||||
roll[idx] = 0
|
||||
else:
|
||||
roll = [0,0]
|
||||
|
|
@ -43,8 +44,11 @@ class Player:
|
|||
return total_moves, roll, return_board
|
||||
|
||||
def make_human_move(self, board, roll):
|
||||
total_moves = roll[0] + roll[1] if roll[0] != roll[1] else int(roll[0])*4
|
||||
move = ""
|
||||
is_quad = roll[0] == roll[1]
|
||||
total_moves = roll[0] + roll[1] if not is_quad else int(roll[0])*4
|
||||
if is_quad:
|
||||
roll = [roll[0]]*4
|
||||
|
||||
while total_moves != 0:
|
||||
while True:
|
||||
print("You have {roll} left!".format(roll=total_moves))
|
||||
|
|
@ -60,6 +64,6 @@ class Player:
|
|||
print("The correct syntax is: 2/5 for a move from index 2 to 5.")
|
||||
|
||||
to_board = Board.apply_moves_to_board(board, self.get_sym(), move)
|
||||
total_moves, roll, board = self.tmp_name(board, to_board, list(roll), self.get_sym(), total_moves)
|
||||
total_moves, roll, board = self.tmp_name(board, to_board, list(roll), self.get_sym(), total_moves, is_quad)
|
||||
print(Board.pretty(board))
|
||||
return board
|
||||
Loading…
Reference in New Issue
Block a user