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How to use show_policy method in tempest

Best Python code snippet using tempest_python

q_solution.py

Source:q_solution.py

1import numpy as np2import time3class AgentQ:4    def __init__(self, env, policy="epsilon_greedy", epsilon=0.05, alpha=0.1, gamma=1):5        self.env = env6        self.q_table = np.zeros(shape=(self.env.num_states, self.env.num_actions))7        self.policy = policy8        self.epsilon = epsilon9        self.alpha = alpha10        self.gamma = gamma11    def choose_action(self):12        if self.policy == "epsilon_greedy" and np.random.uniform(0, 1) < self.epsilon:13            action = np.random.randint(0, self.env.num_actions)14        else:15            state = self.env.agent_position16            q_values_of_state = self.q_table[state, :]17            # Choose randomly AMONG maximum Q-values18            max_q_value = np.max(q_values_of_state)19            maximum_q_values = np.nonzero(q_values_of_state == max_q_value)[0]20            action = np.random.choice(maximum_q_values)21        return action22    def learn(self, old_state, reward, new_state, action):23        max_q_value_in_new_state = np.max(self.q_table[new_state, :])24        current_q_value = self.q_table[old_state, action]25        self.q_table[old_state, action] = (1 - self.alpha) * current_q_value + self.alpha * (reward + self.gamma * max_q_value_in_new_state)26def q_learning(env, agent, num_episodes=500, max_steps_per_episode=1000, learn=True, seconds_between_each_step=0,27               show_grid=False, show_policy=False, show_q_values=False, show_softmax=False, show_learning_curve=False,28               fig_size=6):29    reward_per_episode = np.zeros(num_episodes)30    for episode in range(0, num_episodes):31        cumulative_reward = 032        step = 033        game_over = False34        while step < max_steps_per_episode and not game_over:35            time.sleep(seconds_between_each_step)36            if show_grid or show_learning_curve:37                env.visualize(show_grid=show_grid, show_policy=show_policy,38                              show_learning_curve=show_learning_curve,39                              show_q_values=show_q_values, clear_the_output=True,40                              episode=episode, reward_per_episode=reward_per_episode,41                              agent_q_table=agent.q_table, fig_size=fig_size)42            old_state = env.agent_position43            action = agent.choose_action()44            reward, new_state = env.make_step(action)45            if learn:46                agent.learn(old_state, reward, new_state, action)47            cumulative_reward += reward48            step += 149            # Check whether agent is at terminal state. If yes: end episode; reset agent.50            if env.is_terminal_state():51                time.sleep(seconds_between_each_step)52                if show_grid or show_learning_curve:53                    env.visualize(show_grid=show_grid, show_policy=show_policy,54                                  show_learning_curve=show_learning_curve,55                                  show_q_values=show_q_values, clear_the_output=True,56                                  episode=episode, reward_per_episode=reward_per_episode,57                                  agent_q_table=agent.q_table, fig_size=fig_size)58                env.reset()59                game_over = True60        reward_per_episode[episode] = cumulative_reward...

game_log_viewer.py

Source:game_log_viewer.py

...38			for i in range(int(prisoner_plane[0][0])):39				game.place_piece( MiniShogi.Piece(pieceType,   None, False, player) )40	window.draw_board(game)41	return game42def show_policy(window, policy, game, player):43	legal_moves = game.all_legal_moves(player)44	move_list = []45	for m in legal_moves:46		move_prob = policy[AlphaMiniShogiSearchTree.get_output_index( m, player )]47		move_list.append( (move_prob, m) )48	move_list.sort(reverse=True, key=lambda m:m[0])49	clear_moves = True50	for m in move_list:51		window.draw_move(m[1], clear_moves, m[0])52		print(m)53		clear_moves = False54def show_game_log(window, game_log, index):55	game_log_x = np.moveaxis(game_log['x'][index], -1, 0)56	print("Reward: ", game_log['y'][1][index])57	game = resort_game(window, game_log_x.tolist())58	# policy, reward = AlphaMiniShogiSearchTree(game, best_net_so_far).predict()59	# print("Model Reward: ", reward)60	# print("Player 0 moves:")61	# show_policy(window, game_log['y'][0][index], game, 0)62	# print("Player 0 net moves:")63	# show_policy(window, policy, game, 0)64	65	# print("Player 1 moves:")66	show_policy(window, game_log['y'][0][index], game, 1)67	# print("Player 1 net moves:")68	# show_policy(window, policy, game, 1)69	70	71best_net_so_far = AlphaGoZeroModel(72		input_board_size=MiniShogi.SIZE,73		number_of_input_planes=6*2*2+4*2,74		policy_output_size=MiniShogi.SIZE*(MiniShogi.SIZE+1)*(MiniShogi.SIZE*MiniShogi.SIZE+6),75		number_of_filters=64,76		number_of_residual_block=20,77		value_head_hidden_layer_size=6478	).init_model()79#net_files = glob.glob(f'model_minishogi_*')80#if net_files:81#	lastest_model_file = max(net_files)82#	print(f"Lastest net: {lastest_model_file}")...

policy_show.py

Source:policy_show.py

...25|   -s,--secured=MODE      HTTPS mode "self" or "CA" [OPTIONAL].26|   -v,--verbose           verbose mode[OPTIONAL].27* outputs:28    * Status of the AG policies29.. function:: policy_show.show_policy(session)30    * Display the status of the AG policies.31        Example usage of the method::32            ret = policy_show.show_policy(session)33            print (ret)34        Details::35            policy_obj = policy()36            result = policy_obj.get(session)37        * inputs:38            :param session: session returned by login.39        * outputs:40            :rtype: dictionary of return status matching rest response41        *use cases*42        1. Retrieve the AG policy information.43"""44import sys45from pyfos import pyfos_auth46from pyfos import pyfos_util47from pyfos.utils import brcd_util48from pyfos.pyfos_brocade_access_gateway import policy49def show_policy(session):50    policy_obj = policy()51    # pyfos_util.response_print(policy_obj)52    result = policy_obj.get(session)53    return result54def main(argv):55    # Print arguments56    # print(sys.argv[1:])57    filters = []58    inputs = brcd_util.parse(argv, policy, filters)59    session = brcd_util.getsession(inputs)60    # pyfos_util.response_print(inputs['utilobject'].displaycustomcli())61    result = show_policy(inputs['session'])62    pyfos_util.response_print(result)63    pyfos_auth.logout(session)64if __name__ == "__main__":...

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