def __init__(self,
env,
gamma,
target_update_freq,
num_burn_in,
train_freq,
batch_size,
mode,
log_parent_dir = '/data/datasets/ratneshm/deeprl_hw2/q5'):
self.env_string = env
self.env = gym.make(env)
self.num_actions = self.env.action_space.n
self.gamma = gamma
self.target_update_freq = target_update_freq
self.num_burn_in = num_burn_in
self.train_freq = train_freq
self.batch_size = batch_size
self.iter_ctr = 0
self.eval_episode_ctr = 0
self.preprocessor = preprocessors.PreprocessorSequence()
# loggers
self.qavg_list = np.array([0])
self.reward_list = []
self.loss_log = []
self.loss_last = None
self.mode = mode
self.log_parent_dir = log_parent_dir
self.make_log_dir() # makes empty dir and logfiles based on current timestamp inside self.log_parent_dir
def __init__(self,
env,
gamma,
target_update_freq,
num_burn_in,
train_freq,
batch_size,
mode,
eval_dir=None,
resume_dir=None,
log_parent_dir='/home/vaibhav/madratman/logs/project/dqn'):
self.env_string = env
self.env = gym.make(env)
self.num_actions = self.env.action_space.n
self.gamma = gamma
self.target_update_freq = target_update_freq
self.num_burn_in = num_burn_in
self.train_freq = train_freq
self.batch_size = batch_size
self.train_iter_ctr = 0
self.eval_episode_ctr = 0
self.preprocessor = preprocessors.PreprocessorSequence()
self.eval_dir = eval_dir
self.resume_dir = resume_dir
self.is_eval = False # eval only mode
self.is_resume = False
if eval_dir is not None:
self.is_eval = True
if resume_dir is not None:
self.is_resume = True
# loggers
self.qavg_list = np.array([0])
self.reward_list = []
self.loss_log = []
self.loss_last = None
self.mode = mode
self.log_parent_dir = log_parent_dir
self.make_log_dir(
) # makes empty dir and logfiles based on current timestamp inside self.log_parent_dir
self.start_time = timeit.default_timer()
# printing
self.RED = '\033[91m'
self.BOLD = '\033[1m'
self.ENDC = '\033[0m'
self.LINE = "%s%s##############################################################################%s" % (
self.RED, self.BOLD, self.ENDC)
def __init__(self,
env,
gamma,
target_update_freq,
num_burn_in,
train_freq,
batch_size,
mode,
resume_dir,
log_parent_dir='/home/vaibhav/madratman/logs/project/dqn'):
self.env_string = env
self.env = gym.make(env)
self.num_actions = self.env.action_space.n
self.gamma = gamma
self.target_update_freq = target_update_freq
self.num_burn_in = num_burn_in
self.train_freq = train_freq
self.batch_size = batch_size
if resume_dir is None:
self.train_iter_ctr = 0
self.eval_episode_ctr = 0
self.preprocessor = preprocessors.PreprocessorSequence()
# loggers
self.qavg_list = np.array([0])
self.reward_list = []
self.loss_log = []
self.loss_last = None
self.mode = mode
self.log_parent_dir = log_parent_dir
# self.make_log_dir() # makes empty dir and logfiles based on current timestamp inside self.log_parent_dir
if resume_dir is not None:
print "resuming from ", resume_dir
self.resume_from_log_dir(resume_dir)
self.is_resume = True
print "self.is_resume", self.is_resume
def evaluate(self, num_episodes, max_episode_length=None, gen_video=False):
self.compile(self.is_resume)
evaluation_policy = GreedyPolicy()
eval_preprocessor = preprocessors.PreprocessorSequence()
# env_valid = gym.make(self.env_string)
iter_ctr_valid = 0
Q_sum = 0
eval_episode_ctr_valid = 0
total_reward_all_episodes = []
# https://github.com/openai/gym/blob/master/gym/wrappers/monitoring.py video_callable takes function as arg. so we hack with true lambda
# https://github.com/openai/gym/issues/494
if gen_video:
video_dir = os.path.join(self.log_dir, 'gym_monitor',
str(self.train_iter_ctr).zfill(7))
os.makedirs(video_dir)
env_valid = wrappers.Monitor(env_valid,
video_dir,
video_callable=lambda x: True,
mode='evaluation')
RED = '\033[91m'
BOLD = '\033[1m'
ENDC = '\033[0m'
LINE = "%s%s##############################################################################%s" % (
RED, BOLD, ENDC)
while eval_episode_ctr_valid < num_episodes:
state = self.env.reset()
eval_preprocessor.reset_history_memory()
num_timesteps_in_curr_episode = 0
total_reward_curr_episode = 0.0
while num_timesteps_in_curr_episode < max_episode_length:
num_timesteps_in_curr_episode += 1
iter_ctr_valid += 1
state_network = self.preprocessor.process_state_for_network(
state)
q_values = self.calc_q_values(state_network)
Q_sum += np.max(q_values) # todo fix this
action = evaluation_policy.select_action(q_values)
next_state, reward, is_terminal, _ = self.env.step(action)
total_reward_curr_episode += reward
# print "Evalution : timestep {}, episode {}, action {}, reward {}, total_reward {}"\
# .format(iter_ctr_valid, eval_episode_ctr_valid, action, reward, total_reward_curr_episode)
if is_terminal or (num_timesteps_in_curr_episode >
max_episode_length - 1):
eval_episode_ctr_valid += 1
str_1 = "Evaluate() : iter_ctr_valid {}, eval_episode_ctr_valid : {}, total_reward_curr_episode : {}, num_timesteps_in_curr_episode {}"\
.format(iter_ctr_valid, eval_episode_ctr_valid, total_reward_curr_episode, num_timesteps_in_curr_episode)
msg = "\n%s\n" % (LINE)
msg += "%s%s\n" % (BOLD, str_1)
msg += "%s\n" % (LINE)
print(str(msg))
total_reward_all_episodes.append(total_reward_curr_episode)
# num_timesteps_in_curr_episode = 0
break
state = next_state
Q_avg = Q_sum / float(iter_ctr_valid)
print " sum(total_reward_all_episodes) : {} , float(len(total_reward_all_episodes)) : {}".format\
(sum(total_reward_all_episodes), float(len(total_reward_all_episodes)))
all_episode_avg_reward = sum(total_reward_all_episodes) / float(
len(total_reward_all_episodes))
with tf.name_scope('summaries'):
self.tf_log_scaler(tag='test_mean_avg_reward',
value=all_episode_avg_reward,
step=self.train_iter_ctr)
self.tf_log_scaler(tag='test_mean_Q_max',
value=Q_avg,
step=self.train_iter_ctr)
self.dump_test_episode_reward(all_episode_avg_reward)
self.qavg_list = np.append(self.qavg_list, Q_avg)
self.reward_list.append(all_episode_avg_reward)
print "all_episode_avg_reward ", all_episode_avg_reward
print "\n\n\n self.reward_list \n\n\n", self.reward_list
def evaluate(self, num_episodes, max_episode_length=None, gen_video=False):
"""Test your agent with a provided environment.
You shouldn't update your network parameters here. Also if you
have any layers that vary in behavior between train/test time
(such as dropout or batch norm), you should set them to test.
Basically run your policy on the environment and collect stats
like cumulative reward, average episode length, etc.
You can also call the render function here if you want to
visually inspect your policy.
"""
evaluation_policy = GreedyPolicy()
eval_preprocessor = preprocessors.PreprocessorSequence()
env_valid = gym.make(self.env_string)
iter_ctr_valid = 0
Q_sum = 0
eval_episode_ctr_valid = 0
total_reward_all_episodes = []
# https://github.com/openai/gym/blob/master/gym/wrappers/monitoring.py video_callable takes function as arg. so we hack with true lambda
# https://github.com/openai/gym/issues/494
# if gen_video:
# video_dir = os.path.join(self.log_dir, 'gym_monitor', str(self.iter_ctr).zfill(7))
# os.makedirs(video_dir)
# env_valid = wrappers.Monitor(env_valid, video_dir, video_callable=lambda x:True, mode='evaluation')
while eval_episode_ctr_valid < num_episodes:
state = env_valid.reset()
eval_preprocessor.reset_history_memory()
num_timesteps_in_curr_episode = 0
total_reward_curr_episode = 0.0
while num_timesteps_in_curr_episode < max_episode_length:
num_timesteps_in_curr_episode += 1
iter_ctr_valid += 1
state_network = self.preprocessor.process_state_for_network(state)
q_values = self.calc_q_values(state_network)
Q_sum += np.max(q_values) # todo fix this
action = evaluation_policy.select_action(q_values)
next_state, reward, is_terminal, _ = env_valid.step(action)
total_reward_curr_episode += reward
# print "Evalution : timestep {}, episode {}, action {}, reward {}, total_reward {}"\
# .format(iter_ctr_valid, eval_episode_ctr_valid, action, reward, total_reward_curr_episode)
if is_terminal or (num_timesteps_in_curr_episode > max_episode_length-1):
eval_episode_ctr_valid += 1
print "Evaluate() : iter_ctr_valid {}, eval_episode_ctr_valid : {}, total_reward_curr_episode : {}, num_timesteps_in_curr_episode {}"\
.format(iter_ctr_valid, eval_episode_ctr_valid, total_reward_curr_episode, num_timesteps_in_curr_episode)
total_reward_all_episodes.append(total_reward_curr_episode)
# num_timesteps_in_curr_episode = 0
break
state = next_state
Q_avg = Q_sum/float(iter_ctr_valid)
print " sum(total_reward_all_episodes) : {} , float(len(total_reward_all_episodes)) : {}".format\
(sum(total_reward_all_episodes), float(len(total_reward_all_episodes)))
all_episode_avg_reward = sum(total_reward_all_episodes)/float(len(total_reward_all_episodes))
all_episode_avg_reward_np = np.array(total_reward_all_episodes)
mean_calc=np.mean(all_episode_avg_reward_np)
std_calc=np.std(all_episode_avg_reward_np)
# print all_episode_avg_reward_np.dtype
# print all_episode_avg_reward_np.astype(int).std()
# print all_episode_avg_reward_np.mean()
print "-------"
print "MEAN===", mean_calc
print "STD===", std_calc
print "-------"
# with tf.name_scope('summaries'):
# self.tf_log_scaler(tag='test_mean_avg_reward', value=all_episode_avg_reward, step=self.iter_ctr)
# self.tf_log_scaler(tag='test_mean_Q_max', value=Q_avg, step=self.iter_ctr)
# self.dump_test_episode_reward(all_episode_avg_reward)
# self.qavg_list = np.append(self.qavg_list, Q_avg)
# self.reward_list.append(all_episode_avg_reward)
# pkl.dump(self.reward_list, open("/data/datasets/ratneshm/deeprl_hw2/eval_rewards.pkl", "wb"))
print "all_episode_avg_reward ", all_episode_avg_reward
print "\n\n\n self.reward_list \n\n\n", self.reward_list
def evaluate(self, num_episodes, max_episode_length=None, gen_video=False):
"""Test your agent with a provided environment.
You shouldn't update your network parameters here. Also if you
have any layers that vary in behavior between train/test time
(such as dropout or batch norm), you should set them to test.
Basically run your policy on the environment and collect stats
like cumulative reward, average episode length, etc.
You can also call the render function here if you want to
visually inspect your policy.
"""
if self.is_eval:
self.compile() # load save weights
evaluation_policy = GreedyPolicy()
eval_preprocessor = preprocessors.PreprocessorSequence()
# env_valid = gym.make(self.env_string)
iter_ctr_valid = 0
Q_sum = 0
eval_episode_ctr_valid = 0
total_reward_all_episodes = []
# https://github.com/openai/gym/blob/master/gym/wrappers/monitoring.py video_callable takes function as arg. so we hack with true lambda
# https://github.com/openai/gym/issues/494
if gen_video:
video_dir = os.path.join(self.log_dir, 'gym_monitor',
str(self.train_iter_ctr).zfill(7))
os.makedirs(video_dir)
env_valid = wrappers.Monitor(env_valid,
video_dir,
video_callable=lambda x: True,
mode='evaluation')
RED = '\033[91m'
BOLD = '\033[1m'
ENDC = '\033[0m'
LINE = "%s%s##############################################################################%s" % (
RED, BOLD, ENDC)
while eval_episode_ctr_valid < num_episodes:
state = self.env.reset()
eval_preprocessor.reset_history_memory()
num_timesteps_in_curr_episode = 0
total_reward_curr_episode = 0.0
while num_timesteps_in_curr_episode < max_episode_length:
num_timesteps_in_curr_episode += 1
iter_ctr_valid += 1
state_network = self.preprocessor.process_state_for_network(
state)
q_values = self.calc_q_values(state_network)
Q_sum += np.max(q_values) # todo fix this
action = evaluation_policy.select_action(q_values)
next_state, reward, is_terminal, _ = self.env.step(action)
total_reward_curr_episode += reward
# print "Evalution : timestep {}, episode {}, action {}, reward {}, total_reward {}"\
# .format(iter_ctr_valid, eval_episode_ctr_valid, action, reward, total_reward_curr_episode)
if is_terminal or (num_timesteps_in_curr_episode >
max_episode_length - 1):
eval_episode_ctr_valid += 1
str_1 = "Evaluate() : iter_ctr_valid {}, eval_episode_ctr_valid : {}, total_reward_curr_episode : {:.2f}, num_timesteps_in_curr_episode {}"\
.format(iter_ctr_valid, eval_episode_ctr_valid, total_reward_curr_episode, num_timesteps_in_curr_episode)
msg = "\n%s\n" % (self.LINE) + "%s%s\n" % (
self.BOLD, str_1) + "%s\n" % (self.LINE)
print(str(msg))
total_reward_all_episodes.append(total_reward_curr_episode)
# num_timesteps_in_curr_episode = 0
break
state = next_state
Q_avg = Q_sum / float(iter_ctr_valid)
print " sum(total_reward_all_episodes) : {} , float(len(total_reward_all_episodes)) : {}".format\
(sum(total_reward_all_episodes), float(len(total_reward_all_episodes)))
all_episode_avg_reward = sum(total_reward_all_episodes) / float(
len(total_reward_all_episodes))
with tf.name_scope('summaries'):
self.tf_log_scaler(tag='test_mean_avg_reward',
value=all_episode_avg_reward,
step=self.train_iter_ctr)
self.tf_log_scaler(tag='test_mean_Q_max',
value=Q_avg,
step=self.train_iter_ctr)
self.dump_test_episode_reward(all_episode_avg_reward)
self.qavg_list = np.append(self.qavg_list, Q_avg)
self.reward_list.append(all_episode_avg_reward)
print "all_episode_avg_reward ", all_episode_avg_reward
print "\n\n\n self.reward_list \n\n\n", self.reward_list
