batch_size=config['batch_size'],
state_shape=config['model_state_shape']+[config['state_time']],
output_state_shape=3,
name='Model_ant_new',
summaries=True,
**config['q_params']
)
agent.session.run(tf.global_variables_initializer())
'''
reward_list = []
for episode in range(config['episodes']):
# Store the rewards...
cur_trng_reward, loss = agent.train_episode()
agent._update_training_reward(cur_trng_reward)
reward_list.append(cur_trng_reward)
print(
'episode: %d, step: %d, eps: %.4f, model loss (ant, ball, pro): %.4f, %.4f, %.4f'
% (episode, agent.steps, agent.epsilon, loss[0], loss[1], loss[2]))
if episode > 10:
del reward_list[0]
avg_trng_reward = np.mean(reward_list)
if episode % config['episodes_validate'] == 0 and episode != 0:
agent.validate_model(epsilon=0.05)
config = object_seaquest_config
log_dir = get_log_dir('log', config['game'] + '_' +
str(config['double_q'])) # Name of logging directory
agent = QAgent(config=config, log_dir=log_dir)
saver = tf.train.Saver(max_to_keep=None)
#save.restore(agent.session,'%s/episode_%d.ckpt'%(log_dir,episode))
reward_list = []
grad_list = []
avg_trng_reward = 0
avg_grad = 0
for episode in range(config['episodes']):
print(
'\nepisode: %d, step: %d, eps: %.4f, avg_reward: %.4f, avg_grad: %.4f\n\n---------------------'
% (episode, agent.steps, agent.epsilon, avg_trng_reward, avg_grad))
# Store the rewards...
cur_trng_reward, grad_cur = agent.train_episode()
agent._update_training_reward(cur_trng_reward)
reward_list.append(cur_trng_reward)
if grad_cur > 0:
grad_list.append(grad_cur)
avg_grad = np.mean(grad_list)
if episode > 10:
del reward_list[0]
if len(grad_list) > 10:
del grad_list[0]
avg_trng_reward = np.mean(reward_list)
tol = 1e-5
if episode % config['episodes_validate'] == 0 and episode != 0:
#if agent.steps % config['steps_validate'] == 0:
from agent import QAgent
from configs import pong_config, object_pong_config, breakout_config
from util import get_log_dir
if __name__ == '__main__':
config = object_pong_config
log_dir = get_log_dir('log', config['game']+'_'+str(config['double_q'])) # Name of logging directory
agent = QAgent(config=config, log_dir=log_dir)
saver = tf.train.Saver(max_to_keep=None)
reward_list = []
for episode in range(config['episodes']):
print('episode: %d, step: %d, eps: %.4f' % (episode, agent.steps, agent.epsilon))
# Store the rewards...
cur_trng_reward = agent.train_episode()
agent._update_training_reward(cur_trng_reward)
reward_list.append(cur_trng_reward)
if episode > 10:
del reward_list[0]
avg_trng_reward = np.mean(reward_list)
if episode % config['episodes_validate']==0 and episode != 0:
#if agent.steps % config['steps_validate'] == 0:
print('Validate....\n==============')
scores = [agent.validate_episode(epsilon=0.0) for i in range(config['episodes_validate_runs'])]
agent._update_validation_reward(np.mean(scores))
print(scores)
f = open('learning_curves/trial13/rewards6.txt', 'a')
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from agent import QAgent
from configs import pong_config, breakout_config
from util import get_log_dir
if __name__ == '__main__':
config = breakout_config
log_dir = get_log_dir('log', config['game']+'_'+str(config['double_q']))
agent = QAgent(config=config, log_dir=log_dir)
saver = tf.train.Saver()
for episode in range(config['episodes']):
print('\n\nepisode: %d, step: %d, eps: %.4f\n\n---------------------' % (episode, agent.steps, agent.epsilon))
# Store the rewards...
agent._update_training_reward(agent.train_episode())
if episode % config['episodes_validate']==0:
print('Validate....\n==============')
scores = [agent.validate_episode(epsilon=0.05) for i in range(config['episodes_validate_runs'])]
agent._update_validation_reward(np.mean(scores))
print(scores)
# Store every validation interval
if episode % config['episodes_save_interval']==0:
saver.save(agent.session,'%s/episode_%d.ckpt'%(log_dir,episode))