def test_add_point(self):
logger = Logger(xlabel="x", ylabel="y", legend="test", csv_path="./newtest/test_csv.csv")
logger.add_point(x=1, y=1)
self.assertEqual(logger.xs[0], 1)
self.assertEqual(logger.ys[0], 1)
with self.assertRaises(ValueError):
logger.add_point(None, None)
def test_add_point(self):
logger = Logger(xlabel="x",
ylabel="y",
legend="test",
csv_path="./newtest/test_csv.csv")
logger.add_point(x=1, y=1)
self.assertEqual(logger.xs[0], 1)
self.assertEqual(logger.ys[0], 1)
def test_make_plot(self):
logger = Logger(xlabel="x", ylabel="y", legend="test")
for x in range(10):
logger.add_point(x=x, y=x * x)
self.assertEqual(9 * 9, logger.ys[9])
save_path = './newtest/test.png'
save_dir = os.path.dirname(save_path)
if os.path.exists(save_dir):
shutil.rmtree(save_dir)
logger.make_plot(save_path=save_path)
shutil.rmtree(save_dir)
reward = 0
reward_list = []
for eval_episode in range(evaluate_num):
print('\rEPISODE {} - Eval {} over {} - Number of game played {} - {}'.format(episode, eval_episode,
evaluate_num,
total_game_played,
time_difference_good_format(
seconds,
time.time())),
end='')
_, payoffs = eval_env.run(is_training=False)
total_game_played += 1
reward_list.append(payoffs[0])
reward += payoffs[0]
logger.log('\n########## Evaluation - Episode {} ##########'.format(episode))
logger.log('Timestep: {} Average reward is {}'.format(env.timestep, float(reward) / evaluate_num))
# Add point to logger
logger.add_point(x=env.timestep, y=float(reward) / evaluate_num)
# Make plot
if episode % save_plot_every == 0 and episode > 0:
logger.make_plot(save_path=figure_path + str(episode) + '.png')
logger.make_plot_hist(save_path_1=figure_path + str(episode) + '_hist.png',
save_path_2=figure_path + str(episode) + '_freq.png', reward_list=reward_list)
# Make the final plot
logger.make_plot(save_path=figure_path + 'final_' + str(episode) + '.png')
logger.make_plot_hist(save_path_1=figure_path + str(episode) + '_hist.png',
save_path_2=figure_path + str(episode) + '_freq.png', reward_list=reward_list)
_, payoffs = eval_env.run(is_training=False)
total_game_played += 1
reward_random_list.append(payoffs[0])
reward_random += payoffs[0]
taking_list.append(eval_env.game.players[0].taking)
logger_random.log(
'\n########## Evaluation Against Random - Episode {} ##########'
.format(episode))
logger_random.log(
'Timestep: {} Average reward against random is {}'.format(
env.timestep,
float(reward_random) / evaluate_num))
# Add point to logger
logger_random.add_point(x=env.timestep,
y=float(reward_random) / evaluate_num)
# Make plot
logger_random.make_plot(save_path=figure_path_random +
str(episode) + '.png')
logger_random.make_plot_hist(
save_path_1=figure_path_random + str(episode) + '_hist.png',
save_path_2=figure_path_random + str(episode) + '_freq.png',
reward_list=reward_random_list,
taking_list=taking_list)
# Eval against last agent
reward_opponent = 0
reward_opponent_list = []
taking_list = []
eval_env.set_agents([agent] + [opponent_agent] *
seconds,
time.time())),
end='')
_, payoffs = eval_env.run(is_training=False)
total_game_played += 1
reward_random_list.append(payoffs[0])
reward_random += payoffs[0]
taking_list.append(eval_env.game.players[0].taking)
logger_random.log('\n########## Evaluation Against Random - Episode {} ##########'.format(episode))
logger_random.log(
'Timestep: {} Average reward against random is {}'.format(env.timestep,
float(reward_random) / evaluate_num))
# Add point to logger
logger_random.add_point(x=episode, y=float(reward_random) / evaluate_num)
# Make plot
logger_random.make_plot(save_path=figure_path_random + str(episode) + '.png')
logger_random.make_plot_hist(save_path_1=figure_path_random + str(episode) + '_hist.png',
save_path_2=figure_path_random + str(episode) + '_freq.png',
reward_list=reward_random_list, taking_list=taking_list)
print('\rEPISODE {} - Number of game played {} - {}'.format(episode, total_game_played,
time_difference_good_format(seconds,
time.time())),
end='')
# Generate data from the environment
trajectories, _ = env.run(is_training=True)
total_game_played += 1
rl_loss = agents[i].train_rl()
sl_loss = agents[i].train_sl()
print(
'\rINFO - Agent {}, step {}, rl-loss: {}, sl-loss: {}'.
format(i, step_counters[i], rl_loss, sl_loss),
end='')
# Evaluate the performance. Play with random agents.
if episode % evaluate_every == 0:
reward = 0
eval_episode = 0
for eval_episode in range(evaluate_num):
_, payoffs = eval_env.run(is_training=False)
reward += payoffs[0]
logger.log('\n########## Evaluation ##########')
logger.log('episode: {} Average reward is {}'.format(
episode / evaluate_every,
float(reward) / evaluate_num))
# Add point to logger
logger.add_point(x=episode / evaluate_every,
y=float(reward) / evaluate_num)
# Make plot
if episode % save_plot_every == 0 and episode > 0:
logger.make_plot(save_path=figure_path + str(episode) + '.png')
# Make the final plot
logger.make_plot(save_path=figure_path + 'final_' + str(episode) + '.png')
sess.run(tf.compat.v1.global_variables_initializer())
# STATS ON TAKING BID FOR FIRST PLAYER TO SPEAK
print('\n------------------------')
print('---- Stats on Bids -----')
print('------------------------')
for i in range(num_tests):
if i * 100 % num_tests == 0:
print('\rProgress Bids: {}%'.format(int(i * 100 / num_tests)),
end='')
state, player_id = env.init_game()
points_in_hand = get_hand_value(env.game.players[player_id].hand)
bouts_in_hand = get_nb_bouts(env.game.players[player_id].hand)
action = env.decode_action(agent.eval_step(state))
logger_taking.add_point(x=points_in_hand,
y=bouts_in_hand,
z=action.get_bid_order())
# Showing usual results against himself for this agent
print('\n------------------------')
print('---- Stats on Games ----')
print('------------------------')
# Make environment
env = rlcard.make('tarot')
global_step = tf.Variable(0, name='global_step', trainable=False)
sess.run(tf.compat.v1.global_variables_initializer())
for i in range(num_games):
hand_value = dict()
nb_bouts = dict()
initial_hand = dict()
# PRINTS HERE TO FORCE THE CODE TO CONTINUE (WEIRD PROBLEM IS NOTHING IS ASKED TO BE PRINTED HERE)
def test_make_plot(self):
logger = Logger(xlabel="x", ylabel="y", legend="test")
for x in range(10):
logger.add_point(x=x, y=x * x)
self.assertEqual(9 * 9, logger.ys[9])
logger.make_plot(save_path='./newtest/test.png')
print('\rIteration {}'.format(episode), end='\n')
# Evaluate the performance. Play with NFSP agents.
if episode % evaluate_every == 0:
#agent.save() # Save model
reward = 0
for eval_episode in range(evaluate_num):
his, payoffs = eval_env.run(is_training=False)
reward += payoffs[0]
logger_reward.log('\n########## Evaluation ##########')
logger_reward.log('Iteration: {} Average reward is {}'.format(
episode,
float(reward) / evaluate_num))
# Add point to logger
logger_reward.add_point(x=episode, y=float(reward) / evaluate_num)
import time
start = time.perf_counter()
exploitability = agent.compute_exploitability(evaluate_num)
end = time.perf_counter()
logger.log('episode: {} cost {:10}s ,exploitability is {}'.format(
episode, end - start, exploitability))
logger.add_point(x=episode, y=exploitability)
print("\n")
# Make plot
if episode % save_plot_every == 0 and episode > 0:
logger.make_plot(save_path=figure_path + str(episode) + '.png')
logger_reward.make_plot(save_path=figure_path + str(episode) +
'reward' + '.png')
# Make the final plot
is_training=False)
bet_reward += bet_reward_sum
change_reward += change_reward_sum
bet_logger.log('\n########## Evaluation ##########')
bet_logger.log(
'Timestep: {} Average bet reward is {}. Average change reward is {}'
.format(env.timestep,
float(bet_reward) / evaluate_num,
float(change_reward) / evaluate_num))
# send_slack('Episode: {} Average bet reward is {}. Average change reward is {}'.format(episode, float(bet_reward)/evaluate_num, float(change_reward)/evaluate_num))
# Add point to logger
bet_logger.add_point(x=env.timestep,
y=float(bet_reward) / evaluate_num)
change_logger.add_point(x=env.timestep,
y=float(change_reward) / evaluate_num)
# Make plot
if episode % save_plot_every == 0 and episode > 0:
bet_logger.make_plot(save_path=figure_path + 'bet/' +
str(episode) + '.png')
change_logger.make_plot(save_path=figure_path + 'change/' +
str(episode) + '.png')
if episode % checkpoint_every == 0 and episode > 0:
bet_path, change_path = agent.save(checkpoint_path, episode)
print('Saved to {}, {}'.format(bet_path, change_path))
# Make the final plot