示例#1
0
    def uct_search(self, game_state):
        root_node = MCNode(game_state=game_state)
        mc_tree = MCTree(root_node=root_node)

        for sim_num in range(1, self.num_simulations + 1):
            selected_node = self.selection(mc_tree)
            rewards = self.simulation(selected_node)
            mc_tree.backup_rewards(leaf_node=selected_node, rewards=rewards)

        results = []
        for child in mc_tree.root_node.children:
            results.append((child.previous_action, child.visits, child.get_average_reward(root_node.current_player)))

        return results
示例#2
0
def main():
    game_state = sample_game_states[18]

    num_simulations = 100
    ucb_constant = 0.1

    player = UCTPlayer(ucb_const=ucb_constant, num_samples=1, num_simulations=num_simulations)

    root_node = MCNode(game_state=game_state)
    mc_tree = MCTree(root_node=root_node)

    for sim_num in range(1, player.num_simulations + 1):
        selected_node = player.selection(mc_tree)
        rewards = player.simulation(selected_node)
        mc_tree.backup_rewards(leaf_node=selected_node, rewards=rewards)

        mc_tree.visualize_tree(ucb=player.ucb_const,
                               filename="img_{}".format(sim_num))

    filenames = ["img_{}.png".format(i) for i in range(player.num_simulations, 0, -1)]
    gif_name = "Gif_test.gif"
    duration = 0.2
    make_gif(filenames=filenames, outputname=gif_name, duration=duration)

    im = Image.open(gif_name)
    frames = [frame.copy() for frame in ImageSequence.Iterator(im)]
    frames.reverse()
    frames[0].save('reversed.gif', save_all=True, append_images=frames[1:])
示例#3
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    def isuct_search(self, public_info):
        """Perform information set monte carlo tree search with uct policy.
        Returns list of tuples of possible actions and corresponding statistics (action, visit_count, average_reward)"""

        infoset = self.get_infoset(public_info=public_info, hand=self.hand[:])

        root_node = ISMCNode(infoset)
        mc_tree = MCTree(root_node=root_node)

        for sim_num in range(1, self.num_simulations + 1):
            sampled_state = self.sample_game_state(public_info=public_info)

            selected_node, selected_game_state = self.selection(
                mc_tree, sampled_state)
            rewards = self.simulation(selected_game_state)
            mc_tree.backup_rewards(leaf_node=selected_node, rewards=rewards)

        results = []
        for child in mc_tree.root_node.children:
            results.append(
                (child.previous_action, child.visits,
                 child.get_average_reward(root_node.current_player)))

        return results
示例#4
0
def main():
    sim_player_list = [
        NNPlayer(
            game_mode_nn='../players/models/bigger_classifier200.hdf5',
            partner_nn='../players/models/partner_model_wider_data_2.hdf5',
            solo_nn='../players/models/solo_model_wider_data_10.hdf5',
            wenz_nn='../players/models/wenz_model_wider_data_10.hdf5'),
        NNPlayer(
            game_mode_nn='../players/models/bigger_classifier200.hdf5',
            partner_nn='../players/models/partner_model_wider_data_2.hdf5',
            solo_nn='../players/models/solo_model_wider_data_10.hdf5',
            wenz_nn='../players/models/wenz_model_wider_data_10.hdf5'),
        NNPlayer(
            game_mode_nn='../players/models/bigger_classifier200.hdf5',
            partner_nn='../players/models/partner_model_wider_data_2.hdf5',
            solo_nn='../players/models/solo_model_wider_data_10.hdf5',
            wenz_nn='../players/models/wenz_model_wider_data_10.hdf5'),
        NNPlayer(
            game_mode_nn='../players/models/bigger_classifier200.hdf5',
            partner_nn='../players/models/partner_model_wider_data_2.hdf5',
            solo_nn='../players/models/solo_model_wider_data_10.hdf5',
            wenz_nn='../players/models/wenz_model_wider_data_10.hdf5')
    ]

    game_state = sample_game_states[15]

    num_simulations = 100
    ucb_constant = 0.1

    player = UCTPlayer(ucb_const=ucb_constant,
                       num_samples=1,
                       num_simulations=num_simulations,
                       simulation_player_list=None)

    root_node = MCNode(game_state=game_state)
    mc_tree = MCTree(root_node=root_node)

    for sim_num in range(1, player.num_simulations + 1):
        selected_node = player.selection(mc_tree)
        rewards = player.simulation(selected_node)
        mc_tree.backup_rewards(leaf_node=selected_node, rewards=rewards)

    mc_tree.visualize_tree(
        ucb=player.ucb_const,
        filename="Tree_{}nodes{}ucb_const{}game_mode".format(
            num_simulations, ucb_constant, game_state["game_mode"]))
示例#5
0
def main():
    sim_player_list = [NNPlayer(game_mode_nn='../players/models/bigger_classifier200.hdf5',
                                partner_nn='../players/models/partner_model_wider_data_2.hdf5',
                                solo_nn='../players/models/solo_model_wider_data_10.hdf5',
                                wenz_nn='../players/models/wenz_model_wider_data_10.hdf5'),
                       NNPlayer(game_mode_nn='../players/models/bigger_classifier200.hdf5',
                                partner_nn='../players/models/partner_model_wider_data_2.hdf5',
                                solo_nn='../players/models/solo_model_wider_data_10.hdf5',
                                wenz_nn='../players/models/wenz_model_wider_data_10.hdf5'),
                       NNPlayer(game_mode_nn='../players/models/bigger_classifier200.hdf5',
                                partner_nn='../players/models/partner_model_wider_data_2.hdf5',
                                solo_nn='../players/models/solo_model_wider_data_10.hdf5',
                                wenz_nn='../players/models/wenz_model_wider_data_10.hdf5'),
                       NNPlayer(game_mode_nn='../players/models/bigger_classifier200.hdf5',
                                partner_nn='../players/models/partner_model_wider_data_2.hdf5',
                                solo_nn='../players/models/solo_model_wider_data_10.hdf5',
                                wenz_nn='../players/models/wenz_model_wider_data_10.hdf5')]

    cum_depth = 0
    for game_state in sample_game_states:

        num_simulations = 100
        ucb_constant = 0.1

        player = UCTPlayer(ucb_const=ucb_constant,
                           num_samples=1,
                           num_simulations=num_simulations,
                           simulation_player_list=None)

        root_node = MCNode(game_state=game_state)
        mc_tree = MCTree(root_node=root_node)

        for sim_num in range(1, player.num_simulations + 1):
            selected_node = player.selection(mc_tree)
            rewards = player.simulation(selected_node)
            mc_tree.backup_rewards(leaf_node=selected_node, rewards=rewards)

        depth = mc_tree.max_depth()
        cum_depth += depth

    print('Average tree depth without NN:', cum_depth / len(sample_game_states))

    cum_depth_with_nn = 0
    for game_state in sample_game_states:

        num_simulations = 100
        ucb_constant = 0.1

        player = UCTPlayer(ucb_const=ucb_constant,
                           num_samples=1,
                           num_simulations=num_simulations,
                           simulation_player_list=sim_player_list)

        root_node = MCNode(game_state=game_state)
        mc_tree = MCTree(root_node=root_node)

        for sim_num in range(1, player.num_simulations + 1):
            selected_node = player.selection(mc_tree)
            rewards = player.simulation(selected_node)
            mc_tree.backup_rewards(leaf_node=selected_node, rewards=rewards)

        depth = mc_tree.max_depth()
        cum_depth_with_nn += depth

    print('Average tree depth with NN:', cum_depth_with_nn / len(sample_game_states))