class PseudoCountQLearner(ValueBasedLearner, DensityModelMixin):
"""
Based on DQN+CTS model from the paper 'Unifying Count-Based Exploration and Intrinsic Motivation' (https://arxiv.org/abs/1606.01868)
Presently the implementation differs from the paper in that the novelty bonuses are computed online rather than by computing the
prediction gains after the model has been updated with all frames from the episode. Async training with different final epsilon values
tends to produce better results than just using a single actor-learner.
"""
def __init__(self, args):
self.args = args
super(PseudoCountQLearner, self).__init__(args)
self.cts_eta = args.cts_eta
self.cts_beta = args.cts_beta
self.batch_size = args.batch_update_size
self.replay_memory = ReplayMemory(args.replay_size,
self.local_network.get_input_shape(),
self.num_actions)
self._init_density_model(args)
self._double_dqn_op()
def generate_final_epsilon(self):
if self.num_actor_learners == 1:
return self.args.final_epsilon
else:
return super(PseudoCountQLearner, self).generate_final_epsilon()
def _get_summary_vars(self):
q_vars = super(PseudoCountQLearner, self)._get_summary_vars()
bonus_q05 = tf.Variable(0., name='novelty_bonus_q05')
s1 = tf.summary.scalar('Novelty_Bonus_q05_{}'.format(self.actor_id),
bonus_q05)
bonus_q50 = tf.Variable(0., name='novelty_bonus_q50')
s2 = tf.summary.scalar('Novelty_Bonus_q50_{}'.format(self.actor_id),
bonus_q50)
bonus_q95 = tf.Variable(0., name='novelty_bonus_q95')
s3 = tf.summary.scalar('Novelty_Bonus_q95_{}'.format(self.actor_id),
bonus_q95)
augmented_reward = tf.Variable(0., name='augmented_episode_reward')
s4 = tf.summary.scalar(
'Augmented_Episode_Reward_{}'.format(self.actor_id),
augmented_reward)
return q_vars + [bonus_q05, bonus_q50, bonus_q95, augmented_reward]
#TODO: refactor to make this cleaner
def prepare_state(self, state, total_episode_reward, steps_at_last_reward,
ep_t, episode_ave_max_q, episode_over, bonuses,
total_augmented_reward):
# Start a new game on reaching terminal state
if episode_over:
T = self.global_step.value() * self.max_local_steps
t = self.local_step
e_prog = float(t) / self.epsilon_annealing_steps
episode_ave_max_q = episode_ave_max_q / float(ep_t)
s1 = "Q_MAX {0:.4f}".format(episode_ave_max_q)
s2 = "EPS {0:.4f}".format(self.epsilon)
self.scores.insert(0, total_episode_reward)
if len(self.scores) > 100:
self.scores.pop()
logger.info('T{0} / STEP {1} / REWARD {2} / {3} / {4}'.format(
self.actor_id, T, total_episode_reward, s1, s2))
logger.info(
'ID: {0} -- RUNNING AVG: {1:.0f} ± {2:.0f} -- BEST: {3:.0f}'.
format(
self.actor_id,
np.array(self.scores).mean(),
2 * np.array(self.scores).std(),
max(self.scores),
))
self.log_summary(
total_episode_reward,
episode_ave_max_q,
self.epsilon,
np.percentile(bonuses, 5),
np.percentile(bonuses, 50),
np.percentile(bonuses, 95),
total_augmented_reward,
)
state = self.emulator.get_initial_state()
ep_t = 0
total_episode_reward = 0
episode_ave_max_q = 0
episode_over = False
return (state, total_episode_reward, steps_at_last_reward, ep_t,
episode_ave_max_q, episode_over)
def _double_dqn_op(self):
q_local_action = tf.cast(
tf.argmax(self.local_network.output_layer, axis=1), tf.int32)
q_target_max = utils.ops.slice_2d(
self.target_network.output_layer,
tf.range(0, self.batch_size),
q_local_action,
)
self.one_step_reward = tf.placeholder(tf.float32,
self.batch_size,
name='one_step_reward')
self.is_terminal = tf.placeholder(tf.bool,
self.batch_size,
name='is_terminal')
self.y_target = self.one_step_reward + self.cts_eta*self.gamma*q_target_max \
* (1 - tf.cast(self.is_terminal, tf.float32))
self.double_dqn_loss = self.local_network._value_function_loss(
self.local_network.q_selected_action -
tf.stop_gradient(self.y_target))
self.double_dqn_grads = tf.gradients(self.double_dqn_loss,
self.local_network.params)
# def batch_update(self):
# if len(self.replay_memory) < self.replay_memory.maxlen//10:
# return
# s_i, a_i, r_i, s_f, is_terminal = self.replay_memory.sample_batch(self.batch_size)
# feed_dict={
# self.one_step_reward: r_i,
# self.target_network.input_ph: s_f,
# self.local_network.input_ph: np.vstack([s_i, s_f]),
# self.local_network.selected_action_ph: np.vstack([a_i, a_i]),
# self.is_terminal: is_terminal
# }
# grads = self.session.run(self.double_dqn_grads, feed_dict=feed_dict)
# self.apply_gradients_to_shared_memory_vars(grads)
def batch_update(self):
if len(self.replay_memory) < self.replay_memory.maxlen // 10:
return
s_i, a_i, r_i, s_f, is_terminal = self.replay_memory.sample_batch(
self.batch_size)
feed_dict = {
self.local_network.input_ph: s_f,
self.target_network.input_ph: s_f,
self.is_terminal: is_terminal,
self.one_step_reward: r_i,
}
y_target = self.session.run(self.y_target, feed_dict=feed_dict)
feed_dict = {
self.local_network.input_ph: s_i,
self.local_network.target_ph: y_target,
self.local_network.selected_action_ph: a_i
}
grads = self.session.run(self.local_network.get_gradients,
feed_dict=feed_dict)
self.apply_gradients_to_shared_memory_vars(grads)
def train(self):
""" Main actor learner loop for n-step Q learning. """
logger.debug("Actor {} resuming at Step {}, {}".format(
self.actor_id, self.global_step.value(), time.ctime()))
s = self.emulator.get_initial_state()
s_batch = list()
a_batch = list()
y_batch = list()
bonuses = deque(maxlen=1000)
episode_over = False
t0 = time.time()
global_steps_at_last_record = self.global_step.value()
while (self.global_step.value() < self.max_global_steps):
# # Sync local learning net with shared mem
# self.sync_net_with_shared_memory(self.local_network, self.learning_vars)
# self.save_vars()
rewards = list()
states = list()
actions = list()
max_q_values = list()
local_step_start = self.local_step
total_episode_reward = 0
total_augmented_reward = 0
episode_ave_max_q = 0
ep_t = 0
while not episode_over:
# Sync local learning net with shared mem
self.sync_net_with_shared_memory(self.local_network,
self.learning_vars)
self.save_vars()
# Choose next action and execute it
a, q_values = self.choose_next_action(s)
new_s, reward, episode_over = self.emulator.next(a)
total_episode_reward += reward
max_q = np.max(q_values)
current_frame = new_s[..., -1]
bonus = self.density_model.update(current_frame)
bonuses.append(bonus)
# Rescale or clip immediate reward
reward = self.rescale_reward(
self.rescale_reward(reward) + bonus)
total_augmented_reward += reward
ep_t += 1
rewards.append(reward)
states.append(s)
actions.append(a)
max_q_values.append(max_q)
s = new_s
self.local_step += 1
episode_ave_max_q += max_q
global_step, _ = self.global_step.increment()
if global_step % self.q_target_update_steps == 0:
self.update_target()
if global_step % self.density_model_update_steps == 0:
self.write_density_model()
# Sync local tensorflow target network params with shared target network params
if self.target_update_flags.updated[self.actor_id] == 1:
self.sync_net_with_shared_memory(self.target_network,
self.target_vars)
self.target_update_flags.updated[self.actor_id] = 0
if self.density_model_update_flags.updated[self.actor_id] == 1:
self.read_density_model()
self.density_model_update_flags.updated[self.actor_id] = 0
if self.local_step % self.q_update_interval == 0:
self.batch_update()
if self.is_master() and (self.local_step % 500 == 0):
bonus_array = np.array(bonuses)
steps = global_step - global_steps_at_last_record
global_steps_at_last_record = global_step
logger.debug(
'Mean Bonus={:.4f} / Max Bonus={:.4f} / STEPS/s={}'.
format(bonus_array.mean(), bonus_array.max(),
steps / float(time.time() - t0)))
t0 = time.time()
else:
#compute monte carlo return
mc_returns = np.zeros((len(rewards), ), dtype=np.float32)
running_total = 0.0
for i, r in enumerate(reversed(rewards)):
running_total = r + self.gamma * running_total
mc_returns[len(rewards) - i - 1] = running_total
mixed_returns = self.cts_eta * np.asarray(rewards) + (
1 - self.cts_eta) * mc_returns
#update replay memory
states.append(new_s)
episode_length = len(rewards)
for i in range(episode_length):
self.replay_memory.append(states[i], actions[i],
mixed_returns[i],
i + 1 == episode_length)
s, total_episode_reward, _, ep_t, episode_ave_max_q, episode_over = \
self.prepare_state(s, total_episode_reward, self.local_step, ep_t, episode_ave_max_q, episode_over, bonuses, total_augmented_reward)
class BasePGQLearner(BaseA3CLearner):
def __init__(self, args):
super(BasePGQLearner, self).__init__(args)
self.q_update_counter = 0
self.replay_size = args.replay_size
self.pgq_fraction = args.pgq_fraction
self.batch_update_size = args.batch_update_size
scope_name = 'local_learning_{}'.format(self.actor_id)
conf_learning = {'name': scope_name,
'input_shape': self.input_shape,
'num_act': self.num_actions,
'args': args}
with tf.device('/cpu:0'):
self.local_network = PolicyValueNetwork(conf_learning)
with tf.device('/gpu:0'), tf.variable_scope('', reuse=True):
self.batch_network = PolicyValueNetwork(conf_learning)
self._build_q_ops()
self.reset_hidden_state()
self.replay_memory = ReplayMemory(
self.replay_size,
self.local_network.get_input_shape(),
self.num_actions)
if self.is_master():
var_list = self.local_network.params
self.saver = tf.train.Saver(var_list=var_list, max_to_keep=3,
keep_checkpoint_every_n_hours=2)
def _build_q_ops(self):
# pgq specific initialization
self.pgq_fraction = self.pgq_fraction
self.batch_size = self.batch_update_size
self.q_tilde = self.batch_network.beta * (
self.batch_network.log_output_layer_pi
+ tf.expand_dims(self.batch_network.output_layer_entropy, 1)
) + self.batch_network.output_layer_v
self.Qi, self.Qi_plus_1 = tf.split(axis=0, num_or_size_splits=2, value=self.q_tilde)
self.V, _ = tf.split(axis=0, num_or_size_splits=2, value=self.batch_network.output_layer_v)
self.log_pi, _ = tf.split(axis=0, num_or_size_splits=2, value=tf.expand_dims(self.batch_network.log_output_selected_action, 1))
self.R = tf.placeholder('float32', [None], name='1-step_reward')
self.terminal_indicator = tf.placeholder(tf.float32, [None], name='terminal_indicator')
self.max_TQ = self.gamma*tf.reduce_max(self.Qi_plus_1, 1) * (1 - self.terminal_indicator)
self.Q_a = tf.reduce_sum(self.Qi * tf.split(axis=0, num_or_size_splits=2, value=self.batch_network.selected_action_ph)[0], 1)
self.q_objective = - self.pgq_fraction * tf.reduce_mean(tf.stop_gradient(self.R + self.max_TQ - self.Q_a) * (0.5 * self.V[:, 0] + self.log_pi[:, 0]))
self.V_params = self.batch_network.params
self.q_gradients = tf.gradients(self.q_objective, self.V_params)
self.q_gradients = self.batch_network._clip_grads(self.q_gradients)
def batch_q_update(self):
if len(self.replay_memory) < self.replay_memory.maxlen//10:
return
s_i, a_i, r_i, s_f, is_terminal = self.replay_memory.sample_batch(self.batch_size)
batch_grads = self.session.run(
self.q_gradients,
feed_dict={
self.R: r_i,
self.batch_network.selected_action_ph: np.vstack([a_i, a_i]),
self.batch_network.input_ph: np.vstack([s_i, s_f]),
self.terminal_indicator: is_terminal.astype(np.int),
}
)
self.apply_gradients_to_shared_memory_vars(batch_grads)
class DQNAgent:
def __init__(self, config):
self.config = config
self.logger = logging.getLogger("DQNAgent")
# define models (policy and target)
self.policy_model = DQN(self.config)
self.target_model = DQN(self.config)
# define memory
self.memory = ReplayMemory(self.config)
# define loss
self.loss = HuberLoss()
# define optimizer
self.optim = torch.optim.RMSprop(self.policy_model.parameters())
# define environment
self.env = gym.make('CartPole-v0').unwrapped
self.cartpole = CartPoleEnv(self.config.screen_width)
# initialize counter
self.current_episode = 0
self.current_iteration = 0
self.episode_durations = []
self.batch_size = self.config.batch_size
# set cuda flag
self.is_cuda = torch.cuda.is_available()
if self.is_cuda and not self.config.cuda:
self.logger.info(
"WARNING: You have a CUDA device, so you should probably enable CUDA"
)
self.cuda = self.is_cuda & self.config.cuda
if self.cuda:
self.logger.info("Program will run on *****GPU-CUDA***** ")
print_cuda_statistics()
self.device = torch.device("cuda")
torch.cuda.set_device(self.config.gpu_device)
else:
self.logger.info("Program will run on *****CPU***** ")
self.device = torch.device("cpu")
self.policy_model = self.policy_model.to(self.device)
self.target_model = self.target_model.to(self.device)
self.loss = self.loss.to(self.device)
# Initialize Target model with policy model state dict
self.target_model.load_state_dict(self.policy_model.state_dict())
self.target_model.eval()
# Summary Writer
self.summary_writer = SummaryWriter(log_dir=self.config.summary_dir,
comment='DQN')
def load_checkpoint(self, file_name):
filename = self.config.checkpoint_dir + file_name
try:
self.logger.info("Loading checkpoint '{}'".format(filename))
checkpoint = torch.load(filename)
self.current_episode = checkpoint['episode']
self.current_iteration = checkpoint['iteration']
self.policy_model.load_state_dict(checkpoint['state_dict'])
self.optim.load_state_dict(checkpoint['optimizer'])
self.logger.info(
"Checkpoint loaded successfully from '{}' at (epoch {}) at (iteration {})\n"
.format(self.config.checkpoint_dir, checkpoint['episode'],
checkpoint['iteration']))
except OSError as e:
self.logger.info(
"No checkpoint exists from '{}'. Skipping...".format(
self.config.checkpoint_dir))
self.logger.info("**First time to train**")
def save_checkpoint(self, file_name="checkpoint.pth.tar", is_best=0):
state = {
'episode': self.current_episode,
'iteration': self.current_iteration,
'state_dict': self.policy_model.state_dict(),
'optimizer': self.optim.state_dict(),
}
# Save the state
torch.save(state, self.config.checkpoint_dir + file_name)
# If it is the best copy it to another file 'model_best.pth.tar'
if is_best:
shutil.copyfile(self.config.checkpoint_dir + file_name,
self.config.checkpoint_dir + 'model_best.pth.tar')
def run(self):
"""
This function will the operator
:return:
"""
try:
self.train()
except KeyboardInterrupt:
self.logger.info("You have entered CTRL+C.. Wait to finalize")
def select_action(self, state):
"""
The action selection function, it either uses the model to choose an action or samples one uniformly.
:param state: current state of the model
:return:
"""
if self.cuda:
state = state.cuda()
sample = random.random()
eps_threshold = self.config.eps_start + (
self.config.eps_start - self.config.eps_end) * math.exp(
-1. * self.current_iteration / self.config.eps_decay)
self.current_iteration += 1
if sample > eps_threshold:
with torch.no_grad():
return self.policy_model(state).max(1)[1].view(1,
1) # size (1,1)
else:
return torch.tensor([[random.randrange(2)]],
device=self.device,
dtype=torch.long)
def optimize_policy_model(self):
"""
performs a single step of optimization for the policy model
:return:
"""
if self.memory.length() < self.batch_size:
return
# sample a batch
transitions = self.memory.sample_batch(self.batch_size)
one_batch = Transition(*zip(*transitions))
# create a mask of non-final states
non_final_mask = torch.tensor(tuple(
map(lambda s: s is not None, one_batch.next_state)),
device=self.device,
dtype=torch.uint8) # [128]
non_final_next_states = torch.cat([
s for s in one_batch.next_state if s is not None
]) # [< 128, 3, 40, 80]
# concatenate all batch elements into one
state_batch = torch.cat(one_batch.state) # [128, 3, 40, 80]
action_batch = torch.cat(one_batch.action) # [128, 1]
reward_batch = torch.cat(one_batch.reward) # [128]
state_batch = state_batch.to(self.device)
non_final_next_states = non_final_next_states.to(self.device)
curr_state_values = self.policy_model(state_batch) # [128, 2]
curr_state_action_values = curr_state_values.gather(
1, action_batch) # [128, 1]
# Get V(s_{t+1}) for all next states. By definition we set V(s)=0 if s is a terminal state.
next_state_values = torch.zeros(self.batch_size,
device=self.device) # [128]
next_state_values[non_final_mask] = self.target_model(
non_final_next_states).max(1)[0].detach() # [< 128]
# Get the expected Q values
expected_state_action_values = (
next_state_values * self.config.gamma) + reward_batch # [128]
# compute loss: temporal difference error
loss = self.loss(curr_state_action_values,
expected_state_action_values.unsqueeze(1))
# optimizer step
self.optim.zero_grad()
loss.backward()
for param in self.policy_model.parameters():
param.grad.data.clamp_(-1, 1)
self.optim.step()
return loss
def train(self):
"""
Training loop based on the number of episodes
:return:
"""
for episode in tqdm(
range(self.current_episode, self.config.num_episodes)):
self.current_episode = episode
# reset environment
self.env.reset()
self.train_one_epoch()
# The target network has its weights kept frozen most of the time
if self.current_episode % self.config.target_update == 0:
self.target_model.load_state_dict(
self.policy_model.state_dict())
self.env.render()
self.env.close()
def train_one_epoch(self):
"""
One episode of training; it samples an action, observe next screen and optimize the model once
:return:
"""
episode_duration = 0
prev_frame = self.cartpole.get_screen(self.env)
curr_frame = self.cartpole.get_screen(self.env)
# get state
curr_state = curr_frame - prev_frame
while (1):
episode_duration += 1
# select action
action = self.select_action(curr_state)
# perform action and get reward
_, reward, done, _ = self.env.step(action.item())
if self.cuda:
reward = torch.Tensor([reward]).to(self.device)
else:
reward = torch.Tensor([reward]).to(self.device)
prev_frame = curr_frame
curr_frame = self.cartpole.get_screen(self.env)
# assign next state
if done:
next_state = None
else:
next_state = curr_frame - prev_frame
# add this transition into memory
self.memory.push_transition(curr_state, action, next_state, reward)
curr_state = next_state
# Policy model optimization step
curr_loss = self.optimize_policy_model()
if curr_loss is not None:
if self.cuda:
curr_loss = curr_loss.cpu()
self.summary_writer.add_scalar("Temporal Difference Loss",
curr_loss.detach().numpy(),
self.current_iteration)
# check if done
if done:
break
self.summary_writer.add_scalar("Training Episode Duration",
episode_duration, self.current_episode)
def validate(self):
pass
def finalize(self):
"""
Finalize all the operations of the 2 Main classes of the process the operator and the data loader
:return:
"""
self.logger.info(
"Please wait while finalizing the operation.. Thank you")
self.save_checkpoint()
self.summary_writer.export_scalars_to_json("{}all_scalars.json".format(
self.config.summary_dir))
self.summary_writer.close()
class PseudoCountQLearner(ValueBasedLearner):
def __init__(self, args):
super(PseudoCountQLearner, self).__init__(args)
self.cts_eta = .9
self.batch_size = 32
self.replay_memory = ReplayMemory(args.replay_size)
#more cython tuning could useful here
self.density_model = CTSDensityModel(height=args.cts_rescale_dim,
width=args.cts_rescale_dim,
num_bins=args.cts_bins,
beta=0.05)
def generate_final_epsilon(self):
return 0.1
def _get_summary_vars(self):
q_vars = super(PseudoCountQLearner, self)._get_summary_vars()
bonus_q25 = tf.Variable(0., name='novelty_bonus_q25')
s1 = tf.summary.scalar('Novelty_Bonus_q25_{}'.format(self.actor_id),
bonus_q25)
bonus_q50 = tf.Variable(0., name='novelty_bonus_q50')
s2 = tf.summary.scalar('Novelty_Bonus_q50_{}'.format(self.actor_id),
bonus_q50)
bonus_q75 = tf.Variable(0., name='novelty_bonus_q75')
s3 = tf.summary.scalar('Novelty_Bonus_q75_{}'.format(self.actor_id),
bonus_q75)
return q_vars + [bonus_q25, bonus_q50, bonus_q75]
def prepare_state(self, state, total_episode_reward, steps_at_last_reward,
ep_t, episode_ave_max_q, episode_over, bonuses):
# prevent the agent from getting stuck
reset_game = False
if (self.local_step - steps_at_last_reward > 5000
or (self.emulator.get_lives() == 0
and self.emulator.game not in ONE_LIFE_GAMES)):
steps_at_last_reward = self.local_step
episode_over = True
reset_game = True
# Start a new game on reaching terminal state
if episode_over:
T = self.global_step.value()
t = self.local_step
e_prog = float(t) / self.epsilon_annealing_steps
episode_ave_max_q = episode_ave_max_q / float(ep_t)
s1 = "Q_MAX {0:.4f}".format(episode_ave_max_q)
s2 = "EPS {0:.4f}".format(self.epsilon)
self.scores.insert(0, total_episode_reward)
if len(self.scores) > 100:
self.scores.pop()
logger.info('T{0} / STEP {1} / REWARD {2} / {3} / {4}'.format(
self.actor_id, T, total_episode_reward, s1, s2))
logger.info(
'ID: {0} -- RUNNING AVG: {1:.0f} ± {2:.0f} -- BEST: {3:.0f}'.
format(
self.actor_id,
np.array(self.scores).mean(),
2 * np.array(self.scores).std(),
max(self.scores),
))
if self.is_master() and self.is_train:
stats = [
total_episode_reward,
episode_ave_max_q,
self.epsilon,
np.percentile(bonuses, 25),
np.percentile(bonuses, 50),
np.percentile(bonuses, 75),
]
feed_dict = {
self.summary_ph[i]: stats[i]
for i in range(len(stats))
}
res = self.session.run(self.update_ops + [self.summary_op],
feed_dict=feed_dict)
self.summary_writer.add_summary(res[-1],
self.global_step.value())
if reset_game or self.emulator.game in ONE_LIFE_GAMES:
state = self.emulator.get_initial_state()
ep_t = 0
total_episode_reward = 0
episode_ave_max_q = 0
episode_over = False
return state, total_episode_reward, steps_at_last_reward, ep_t, episode_ave_max_q, episode_over
def batch_update(self):
if len(self.replay_memory) < self.batch_size:
return
s_i, a_i, r_i, s_f, is_terminal = self.replay_memory.sample_batch(
self.batch_size)
q_target_values = self.session.run(
self.target_network.output_layer,
feed_dict={self.target_network.input_ph: s_f})
y_target = r_i + self.cts_eta * self.gamma * q_target_values.max(
axis=1) * (1 - is_terminal.astype(np.int))
feed_dict = {
self.local_network.input_ph: s_i,
self.local_network.target_ph: y_target,
self.local_network.selected_action_ph: a_i
}
grads = self.session.run(self.local_network.get_gradients,
feed_dict=feed_dict)
self.apply_gradients_to_shared_memory_vars(grads)
def _run(self):
""" Main actor learner loop for n-step Q learning. """
if not self.is_train:
return self.test()
logger.debug("Actor {} resuming at Step {}, {}".format(
self.actor_id, self.global_step.value(), time.ctime()))
s = self.emulator.get_initial_state()
s_batch = []
a_batch = []
y_batch = []
bonuses = deque(maxlen=100)
exec_update_target = False
total_episode_reward = 0
episode_ave_max_q = 0
episode_over = False
qmax_down = 0
qmax_up = 0
prev_qmax = -10 * 6
low_qmax = 0
ep_t = 0
t0 = time.time()
while (self.global_step.value() < self.max_global_steps):
# Sync local learning net with shared mem
self.sync_net_with_shared_memory(self.local_network,
self.learning_vars)
self.save_vars()
rewards = []
states = []
actions = []
local_step_start = self.local_step
while not episode_over:
# Choose next action and execute it
a, readout_t = self.choose_next_action(s)
new_s, reward, episode_over = self.emulator.next(a)
total_episode_reward += reward
current_frame = new_s[..., -1]
bonus = self.density_model.update(current_frame)
bonuses.append(bonus)
if self.is_master() and (self.local_step % 200 == 0):
bonus_array = np.array(bonuses)
logger.debug(
'Mean Bonus={:.4f} / Max Bonus={:.4f} / STEPS/s={}'.
format(bonus_array.mean(), bonus_array.max(),
100. / (time.time() - t0)))
t0 = time.time()
# Rescale or clip immediate reward
reward = self.rescale_reward(
self.rescale_reward(reward) + bonus)
ep_t += 1
rewards.append(reward)
states.append(s)
actions.append(a)
s = new_s
self.local_step += 1
episode_ave_max_q += np.max(readout_t)
global_step, update_target = self.global_step.increment(
self.q_target_update_steps)
if update_target:
update_target = False
exec_update_target = True
if self.local_step % 4 == 0:
self.batch_update()
self.local_network.global_step = global_step
else:
mc_returns = list()
running_total = 0.0
for r in reversed(rewards):
running_total = r + self.gamma * running_total
mc_returns.insert(0, running_total)
mixed_returns = self.cts_eta * np.array(rewards) + (
1 - self.cts_eta) * np.array(mc_returns)
states.append(new_s)
episode_length = len(rewards)
for i in range(episode_length):
self.replay_memory.append(
(states[i], actions[i], mixed_returns[i],
states[i + 1], i + 1 == episode_length))
if exec_update_target:
self.update_target()
exec_update_target = False
# Sync local tensorflow target network params with shared target network params
if self.target_update_flags.updated[self.actor_id] == 1:
self.sync_net_with_shared_memory(self.target_network,
self.target_vars)
self.target_update_flags.updated[self.actor_id] = 0
s, total_episode_reward, _, ep_t, episode_ave_max_q, episode_over = \
self.prepare_state(s, total_episode_reward, self.local_step, ep_t, episode_ave_max_q, episode_over, bonuses)
class BasePGQLearner(BaseA3CLearner):
def __init__(self, args):
super(BasePGQLearner, self).__init__(args)
# args.entropy_regularisation_strength = 0.0
conf_learning = {
'name': 'local_learning_{}'.format(self.actor_id),
'input_shape': self.input_shape,
'num_act': self.num_actions,
'args': args
}
self.local_network = PolicyValueNetwork(conf_learning)
self.reset_hidden_state()
if self.is_master():
var_list = self.local_network.params
self.saver = tf.train.Saver(var_list=var_list,
max_to_keep=3,
keep_checkpoint_every_n_hours=2)
# pgq specific initialization
self.batch_size = 32
self.pgq_fraction = args.pgq_fraction
self.replay_memory = ReplayMemory(args.replay_size)
self.q_tilde = self.local_network.beta * (
self.local_network.log_output_layer_pi +
tf.expand_dims(self.local_network.output_layer_entropy,
1)) + self.local_network.output_layer_v
self.Qi, self.Qi_plus_1 = tf.split(axis=0,
num_or_size_splits=2,
value=self.q_tilde)
self.V, _ = tf.split(axis=0,
num_or_size_splits=2,
value=self.local_network.output_layer_v)
self.log_pi, _ = tf.split(
axis=0,
num_or_size_splits=2,
value=tf.expand_dims(self.local_network.log_output_selected_action,
1))
self.R = tf.placeholder('float32', [None], name='1-step_reward')
self.terminal_indicator = tf.placeholder(tf.float32, [None],
name='terminal_indicator')
self.max_TQ = self.gamma * tf.reduce_max(
self.Qi_plus_1, 1) * (1 - self.terminal_indicator)
self.Q_a = tf.reduce_sum(
self.Qi * tf.split(axis=0,
num_or_size_splits=2,
value=self.local_network.selected_action_ph)[0],
1)
self.q_objective = -self.pgq_fraction * tf.reduce_mean(
tf.stop_gradient(self.R + self.max_TQ - self.Q_a) *
(self.V[:, 0] + self.log_pi[:, 0]))
self.V_params = self.local_network.params
self.q_gradients = tf.gradients(self.q_objective, self.V_params)
if self.local_network.clip_norm_type == 'global':
self.q_gradients = tf.clip_by_global_norm(
self.q_gradients, self.local_network.clip_norm)[0]
elif self.local_network.clip_norm_type == 'local':
self.q_gradients = [
tf.clip_by_norm(g, self.local_network.clip_norm)
for g in self.q_gradients
]
if (self.optimizer_mode == "local"):
if (self.optimizer_type == "rmsprop"):
self.batch_opt_st = np.ones(size, dtype=ctypes.c_float)
else:
self.batch_opt_st = np.zeros(size, dtype=ctypes.c_float)
elif (self.optimizer_mode == "shared"):
self.batch_opt_st = args.batch_opt_state
def apply_batch_q_update(self):
s_i, a_i, r_i, s_f, is_terminal = self.replay_memory.sample_batch(
self.batch_size)
batch_grads, max_TQ, Q_a = self.session.run(
[self.q_gradients, self.max_TQ, self.Q_a],
feed_dict={
self.R: r_i,
self.local_network.selected_action_ph: np.vstack([a_i, a_i]),
self.local_network.input_ph: np.vstack([s_i, s_f]),
self.terminal_indicator: is_terminal.astype(np.int),
})
# print 'max_TQ={}, Q_a={}'.format(max_TQ[:5], Q_a[:5])
self._apply_gradients_to_shared_memory_vars(batch_grads,
opt_st=self.batch_opt_st)
def softmax(self, x, temperature):
x /= temperature
exp_x = np.exp(x - np.max(x))
return exp_x / exp_x.sum()
class PseudoCountQLearner(ValueBasedLearner):
def __init__(self, args):
super(PseudoCountQLearner, self).__init__(args)
self.cts_eta = .9
self.batch_size = 32
self.replay_memory = ReplayMemory(args.replay_size)
#more cython tuning could useful here
self.density_model = CTSDensityModel(height=args.cts_rescale_dim,
width=args.cts_rescale_dim,
num_bins=args.cts_bins,
beta=0.05)
def generate_final_epsilon(self):
return 0.1
def batch_update(self):
if len(self.replay_memory) < self.batch_size:
return
s_i, a_i, r_i, s_f, is_terminal = self.replay_memory.sample_batch(
self.batch_size)
q_target_values = self.session.run(
self.target_network.output_layer,
feed_dict={self.target_network.input_ph: s_f})
y_target = r_i + self.cts_eta * self.gamma * q_target_values.max(
axis=1) * (1 - is_terminal.astype(np.int))
feed_dict = {
self.local_network.input_ph: s_i,
self.local_network.target_ph: y_target,
self.local_network.selected_action_ph: a_i
}
grads = self.session.run(self.local_network.get_gradients,
feed_dict=feed_dict)
self.apply_gradients_to_shared_memory_vars(grads)
def _run(self):
""" Main actor learner loop for n-step Q learning. """
if not self.is_train:
return self.test()
logger.debug("Actor {} resuming at Step {}, {}".format(
self.actor_id, self.global_step.value(), time.ctime()))
s = self.emulator.get_initial_state()
s_batch = []
a_batch = []
y_batch = []
bonuses = deque(maxlen=100)
exec_update_target = False
total_episode_reward = 0
episode_ave_max_q = 0
episode_over = False
qmax_down = 0
qmax_up = 0
prev_qmax = -10 * 6
low_qmax = 0
ep_t = 0
while (self.global_step.value() < self.max_global_steps):
# Sync local learning net with shared mem
self.sync_net_with_shared_memory(self.local_network,
self.learning_vars)
self.save_vars()
rewards = []
states = []
actions = []
local_step_start = self.local_step
while not episode_over:
# Choose next action and execute it
a, readout_t = self.choose_next_action(s)
new_s, reward, episode_over = self.emulator.next(a)
total_episode_reward += reward
current_frame = new_s[..., -1]
bonus = self.density_model.update(current_frame)
bonuses.append(bonus)
if self.is_master() and (self.local_step % 200 == 0):
bonus_array = np.array(bonuses)
logger.debug('Mean Bonus={:.4f} / Max Bonus={:.4f}'.format(
bonus_array.mean(), bonus_array.max()))
# Rescale or clip immediate reward
# reward = self.rescale_reward(self.rescale_reward(reward) + bonus)
reward = self.rescale_reward(reward)
ep_t += 1
rewards.append(reward)
states.append(s)
actions.append(a)
s = new_s
self.local_step += 1
episode_ave_max_q += np.max(readout_t)
global_step, update_target = self.global_step.increment(
self.q_target_update_steps)
if update_target:
update_target = False
exec_update_target = True
if self.local_step % 4 == 0:
self.batch_update()
self.local_network.global_step = global_step
else:
mc_returns = list()
running_total = 0.0
for r in reversed(rewards):
running_total = r + self.gamma * running_total
mc_returns.insert(0, running_total)
mixed_returns = self.cts_eta * np.array(rewards) + (
1 - self.cts_eta) * np.array(mc_returns)
states.append(new_s)
episode_length = len(rewards)
for i in range(episode_length):
self.replay_memory.append(
(states[i], actions[i], mixed_returns[i],
states[i + 1], i + 1 == episode_length))
if exec_update_target:
self.update_target()
exec_update_target = False
# Sync local tensorflow target network params with shared target network params
if self.target_update_flags.updated[self.actor_id] == 1:
self.sync_net_with_shared_memory(self.target_network,
self.target_vars)
self.target_update_flags.updated[self.actor_id] = 0
s, total_episode_reward, _, ep_t, episode_ave_max_q, episode_over = \
self.prepare_state(s, total_episode_reward, self.local_step, ep_t, episode_ave_max_q, episode_over)
class AElearner(ValueBasedLearner, DensityModelMixinAE):
def __init__(self, args):
self.args = args
super(AElearner, self).__init__(args)
self.cts_eta = args.cts_eta
self.cts_beta = args.cts_beta
self.ae_delta = args.ae_delta
self.batch_size = args.batch_update_size
self.replay_memory = ReplayMemory(
args.replay_size,
self.local_network_upper.get_input_shape(),
# self.local_network.get_input_shape(),
self.num_actions)
#inits desity model(chooses how many steps for update )
#20 * q targt update steps
self._init_density_model(args)
#computes loss
self._double_dqn_op()
self.which_net_to_update_counter = 0
self.ae_counter = 0
self.epsilon_greedy_counter = 0
self.total_ae_counter = 0
self.total_epsilon_greedy_counter = 0
self.q_values_upper_max = []
self.q_values_lower_max = []
self.ae_valid_actions = True
self.action_meanings = self.emulator.env.unwrapped.get_action_meanings(
)
self.minimized_actions_counter = {
value: 0
for value in self.action_meanings
}
print(self.minimized_actions_counter)
# print("In AE class")
def beta_function(self, A, S, delta, k, Vmax, c):
#print (utils.fast_cts.__name__)
#print("This is temp {}".format(temp))
#print("This is k")
#print(k)
if k < 1:
k = 1
# print("c is : {}".format(c))
# print("k is : {}".format(k))
# print("S is : {}".format(S))
# print("A is : {}".format(A))
# print("delta is : {}".format(delta))
# # print("c*(k-1)*(k-1)*S*A is : {}".format(c*(k-1)*(k-1)*S*A))
# print("c*(k1)*(k1)*S*A/delta is : {}".format(c*(k)*(k)*S*A/delta))
# print("math.log(c*(k-1)*(k-1)*S*A/delta is : {}".format(math.log(c*(k)*(k)*S*A/delta)))
# #k = math.maximum(k,1)
# z = 5
# assert(math.isnan(5))
assert (not math.isnan(math.log(
c * k * k * S * A / delta))), "log of left is nan"
left = math.sqrt(k * math.log(c * k * k * S * A / delta))
assert (not math.isnan(left)), " left side of beta is Nan"
if k == 1:
right = 0
else:
right = math.sqrt(
(k - 1) *
math.log(c * (k - 1) *
(k - 1) * S * A / delta)) #the error is here
assert (not math.isnan(right)), " right side of beta is Nan"
beta = k * Vmax * (left - (1 - 1 / k) * right)
assert (not math.isnan(beta)), " right side of beta is Nan"
return beta
### pay attention: call it for upper q
### Returns minimized action pool after AE according to the paper(Q upper is larger than V lower)
def minimize_action_pool(self, state):
new_actions = np.zeros([self.num_actions])
#TODO get q upperbound values
#TODO: target or local ???
# q_values_upper = self.session.run(
# self.target_network_upper.output_layer,
# feed_dict={self.target_network_upper.input_ph: [state]})[0]
# q_values_lower = self.session.run(
# self.target_network_lower.output_layer,
# feed_dict={self.target_network_lower.input_ph: [state]})[0]
q_values_upper = self.session.run(
self.local_network_upper.output_layer,
feed_dict={self.local_network_upper.input_ph: [state]})[0]
q_values_lower = self.session.run(
self.local_network_lower.output_layer,
feed_dict={self.local_network_lower.input_ph: [state]})[0]
#TODO V lower upperbound
Vlow = max(q_values_lower)
Vhigh = max(q_values_upper)
#print("q_values_lower: {} / q_values_upper: {}".format(q_values_lower, q_values_upper))
# print("The value of Vlow is {}".format(Vlow))
for index, action in enumerate(new_actions):
new_actions[index] = q_values_upper[index] >= Vlow
if q_values_upper[index] < Vlow:
self.minimized_actions_counter[
self.action_meanings[index]] += 1
# print("The value of q_values_upper on index: {} is :{}".format(index,q_values_upper[index]))
#print("new actions are: {}".format(new_actions))
#print("new actions array: {}".format(new_actions))
return new_actions, q_values_lower, q_values_upper
def choose_next_action(self, state):
#print("we use our AE new algorithm choose next action")
new_action = np.zeros([self.num_actions])
q_values = self.session.run(
self.local_network_upper.output_layer,
feed_dict={self.local_network_upper.input_ph: [state]})[0]
# q_values_upper = self.session.run(
# self.target_network_upper.output_layer,
# feed_dict={self.target_network_upper.input_ph: [state]})[0]
# q_values_lower = self.session.run(
# self.target_network_lower.output_layer,
# feed_dict={self.target_network_lower.input_ph: [state]})[0]
# Vlow = max(q_values_lower)
# Vhigh = max(q_values_upper)
# print("Vlow is: {}".format(Vlow))
# print("q_upper values: {}".format(q_values_upper))
#self.q_values_lower_max.append(Vlow)
#self.q_values_lower_max.append(Vhigh)
#print("q_upper: {}".format(q_upper_curr))
#print("q_lower: {}".format(q_lower_curr))
secure_random = random.SystemRandom()
action_pool, q_values_lower, q_values_upper = self.minimize_action_pool(
state)
if self.local_step % 500 == 0:
#num_actions_minimized = self.num_actions - np.sum(action_pool)
#minimized_actions = [ self.action_meanings[index] for index,value in enumerate (action_pool) if value == 0 ]
logger.info('Total minimized actions{0} / LOCAL STEP {1} '.format(
self.minimized_actions_counter, self.local_step))
#print("action pool is: {}".format(action_pool))
# print("The action pool {}".format(action_pool))
random_index = secure_random.randrange(0, len(action_pool))
indexes_valid_actions = []
for i, item in enumerate(action_pool):
if item == 1:
indexes_valid_actions.append(i)
#There are no actions after elimination
#Using epsilon greedy from all the actions
if not indexes_valid_actions:
#print("q_values_lower: {} / q_values_upper: {}".format(q_values_lower, q_values_upper))
#print("no valid ae actions!!! - use epsilon greedy")
self.ae_valid_actions = False
self.epsilon_greedy_counter += 1
super_return = super(AElearner, self).choose_next_action(state)
return super_return[0], super_return[
1], q_values_lower, q_values_upper
self.ae_counter += 1
random_index = secure_random.choice(indexes_valid_actions)
new_action[random_index] = 1
self.reduce_thread_epsilon()
#print("succefuly eliminated actions")
#print("new action is: {}".format(new_action))
#print("q_values (upper): {}".format(q_values))
return new_action, q_values, q_values_lower, q_values_upper
def generate_final_epsilon(self):
if self.num_actor_learners == 1:
return self.args.final_epsilon
else:
return super(AElearner, self).generate_final_epsilon()
def _get_summary_vars(self):
q_vars = super(AElearner, self)._get_summary_vars()
bonus_q05 = tf.Variable(0., name='novelty_bonus_q05')
s1 = tf.summary.scalar('Novelty_Bonus_q05_{}'.format(self.actor_id),
bonus_q05)
bonus_q50 = tf.Variable(0., name='novelty_bonus_q50')
s2 = tf.summary.scalar('Novelty_Bonus_q50_{}'.format(self.actor_id),
bonus_q50)
bonus_q95 = tf.Variable(0., name='novelty_bonus_q95')
s3 = tf.summary.scalar('Novelty_Bonus_q95_{}'.format(self.actor_id),
bonus_q95)
augmented_reward = tf.Variable(0., name='augmented_episode_reward')
s4 = tf.summary.scalar(
'Augmented_Episode_Reward_{}'.format(self.actor_id),
augmented_reward)
return q_vars + [bonus_q05, bonus_q50, bonus_q95, augmented_reward]
#TODO: refactor to make this cleaner
def prepare_state(self, state, total_episode_reward, steps_at_last_reward,
ep_t, episode_ave_max_q, episode_over, bonuses,
total_augmented_reward, q_values_lower, q_values_upper):
# Start a new game on reaching terminal state
if episode_over:
T = self.global_step.value() * self.max_local_steps
t = self.local_step
e_prog = float(t) / self.epsilon_annealing_steps
episode_ave_max_q = episode_ave_max_q / float(ep_t)
s1 = "Q_MAX {0:.4f}".format(episode_ave_max_q)
s2 = "EPS {0:.4f}".format(self.epsilon)
self.scores.insert(0, total_episode_reward)
if len(self.scores) > 100:
self.scores.pop()
print("Used AE for {} times".format(self.ae_counter))
print("Used Epsilon greedy for {} times".format(
self.epsilon_greedy_counter))
self.total_ae_counter += self.ae_counter
self.total_epsilon_greedy_counter += self.epsilon_greedy_counter
self.ae_counter = 0
self.epsilon_greedy_counter = 0
print("Total count of use of AE is {} :".format(
self.total_ae_counter))
print("Total count of use of Epsilone Greedy {}".format(
self.total_epsilon_greedy_counter))
logger.info('T{0} / STEP {1} / REWARD {2} / {3} / {4}'.format(
self.actor_id, T, total_episode_reward, s1, s2))
logger.info(
'ID: {0} -- RUNNING AVG: {1:.0f} +- {2:.0f} -- BEST: {3:.0f}'.
format(
self.actor_id,
np.array(self.scores).mean(),
2 * np.array(self.scores).std(),
max(self.scores),
))
logger.info("q_values_lower: {} / q_values_upper: {}".format(
q_values_lower, q_values_upper))
#print(" T type {}".format(type(T)))
self.vis.plot_current_errors(T, total_episode_reward)
#self.vis.plot_total_ae_counter(T,self.minimized_actions_counter, self.action_meanings)
self.vis.plot_q_values(T, q_values_lower, q_values_upper,
self.action_meanings)
self.wr.writerow([T])
self.wr.writerow([total_episode_reward])
#print(" total episode reward type {}".format(type(total_episode_reward)))
#print ('[%s]' % ', '.join(map(str, t.vis.plot_data['X'])))
self.log_summary(
total_episode_reward,
episode_ave_max_q,
self.epsilon,
np.percentile(bonuses, 5),
np.percentile(bonuses, 50),
np.percentile(bonuses, 95),
total_augmented_reward,
)
state = self.emulator.get_initial_state()
ep_t = 0
total_episode_reward = 0
episode_ave_max_q = 0
episode_over = False
return (state, total_episode_reward, steps_at_last_reward, ep_t,
episode_ave_max_q, episode_over)
def _double_dqn_op(self):
q_local_action_lower = tf.cast(
tf.argmax(self.local_network_lower.output_layer, axis=1), tf.int32)
q_target_max_lower = utils.ops.slice_2d(
self.target_network_lower.output_layer,
tf.range(0, self.batch_size),
q_local_action_lower,
)
q_local_action_upper = tf.cast(
tf.argmax(self.local_network_upper.output_layer, axis=1), tf.int32)
q_target_max_upper = utils.ops.slice_2d(
self.target_network_upper.output_layer,
tf.range(0, self.batch_size),
q_local_action_upper,
)
self.one_step_reward = tf.placeholder(tf.float32,
self.batch_size,
name='one_step_reward')
self.is_terminal = tf.placeholder(tf.bool,
self.batch_size,
name='is_terminal')
self.y_target_lower = self.one_step_reward + self.cts_eta*self.gamma*q_target_max_lower \
* (1 - tf.cast(self.is_terminal, tf.float32))
self.y_target_upper = self.one_step_reward + self.cts_eta*self.gamma*q_target_max_upper \
* (1 - tf.cast(self.is_terminal, tf.float32))
self.double_dqn_loss_lower = self.local_network_lower._value_function_loss(
self.local_network_lower.q_selected_action -
tf.stop_gradient(self.y_target_lower))
self.double_dqn_loss_upper = self.local_network_upper._value_function_loss(
self.local_network_upper.q_selected_action -
tf.stop_gradient(self.y_target_upper))
self.double_dqn_grads_lower = tf.gradients(
self.double_dqn_loss_lower, self.local_network_lower.params)
self.double_dqn_grads_upper = tf.gradients(
self.double_dqn_loss_upper, self.local_network_upper.params)
# def batch_update(self):
# if len(self.replay_memory) < self.replay_memory.maxlen//10:
# return
# s_i, a_i, r_i, s_f, is_terminal = self.replay_memory.sample_batch(self.batch_size)
# feed_dict={
# self.one_step_reward: r_i,
# self.target_network.input_ph: s_f,
# self.local_network.input_ph: np.vstack([s_i, s_f]),
# self.local_network.selected_action_ph: np.vstack([a_i, a_i]),
# self.is_terminal: is_terminal
# }
# grads = self.session.run(self.double_dqn_grads, feed_dict=feed_dict)
# self.apply_gradients_to_shared_memory_vars(grads)
def batch_update(self):
if len(self.replay_memory) < self.replay_memory.maxlen // 10:
return
#TODO check if we need two replay memories
s_i, a_i, r_i, s_f, is_terminal, b_i = self.replay_memory.sample_batch(
self.batch_size)
#print("This is b_i {}".format(b_i))
# if(self.which_net_to_update_counter %2):
feed_dict = {
self.local_network_upper.input_ph: s_f,
self.target_network_upper.input_ph: s_f,
self.is_terminal: is_terminal,
self.one_step_reward: r_i + b_i,
}
y_target_upper = self.session.run(self.y_target_upper,
feed_dict=feed_dict)
#print(y_target_upper)
feed_dict = {
self.local_network_upper.input_ph: s_i,
self.local_network_upper.target_ph: y_target_upper,
self.local_network_upper.selected_action_ph: a_i
}
#TODO , the exception of nan happens here.
#print(self.local_network_upper.get_gradients)
grads = self.session.run(self.local_network_upper.get_gradients,
feed_dict=feed_dict)
#assert (not tf.debugging.is_nan(grads)) , " upper local network grads are nan"
self.apply_gradients_to_shared_memory_vars(grads,
upper_or_lower="Upper")
# else:
feed_dict = {
self.local_network_lower.input_ph: s_f,
self.target_network_lower.input_ph: s_f,
self.is_terminal: is_terminal,
self.one_step_reward: r_i - b_i,
}
y_target_lower = self.session.run(self.y_target_lower,
feed_dict=feed_dict)
feed_dict = {
self.local_network_lower.input_ph: s_i,
self.local_network_lower.target_ph: y_target_lower,
self.local_network_lower.selected_action_ph: a_i
}
grads = self.session.run(self.local_network_lower.get_gradients,
feed_dict=feed_dict)
#assert (not tf.debugging.is_nan(grads)) , " lower local network grads are nan"
self.apply_gradients_to_shared_memory_vars(grads,
upper_or_lower="Lower")
def train(self):
""" Main actor learner loop for n-step Q learning. """
logger.debug("Actor {} resuming at Step {}, {}".format(
self.actor_id, self.global_step.value(), time.ctime()))
s = self.emulator.get_initial_state()
# print(" In train of AE")
s_batch = list()
a_batch = list()
y_batch = list()
bonuses = deque(maxlen=1000)
episode_over = False
t0 = time.time()
global_steps_at_last_record = self.global_step.value()
while (self.global_step.value() < self.max_global_steps):
# # Sync local learning net with shared mem
# self.sync_net_with_shared_memory(self.local_network, self.learning_vars)
# self.save_vars()
rewards = list()
states = list()
actions = list()
max_q_values = list()
bonuses = list()
local_step_start = self.local_step
total_episode_reward = 0
total_augmented_reward = 0
episode_ave_max_q = 0
ep_t = 0
action_count = 0
while not episode_over:
A = self.num_actions
S = 100000000
factor_delta_scale_to_1_10 = 100000000000
factor_delta_actual = 100
factor_divide_bonus_scale_to_0_10 = 100000000
factor_divide_bonus = 1000
#S = 2**S
delta = self.ae_delta * factor_delta_actual
#delta = self.ae_delta * factor_delta # experimental: trying to stabilize the bonus along the entire learning
Vmax = 100000
c = 5
# Sync local learning net with shared mem
#TODO: upper / lower
self.sync_net_with_shared_memory(self.local_network_lower,
self.learning_vars_lower)
self.sync_net_with_shared_memory(self.local_network_upper,
self.learning_vars_upper)
self.save_vars()
# Choose next action and execute it
# print("intrinsic motivation print")
a, q_values, q_values_lower, q_values_upper = self.choose_next_action(
s)
action_count += 1
new_s, reward, episode_over = self.emulator.next(a)
total_episode_reward += reward
max_q = np.max(q_values)
prev_s = s
current_frame = new_s[..., -1]
prev_frame = prev_s[..., -1]
#print("This is a {}".format(a))
index_of_a = np.argmax(a)
## TODO change back to update 2 and understand the underlying
## cython code
k = (self.density_model[index_of_a]).update(prev_frame)
#print(type(self.density_model[index_of_a]))
assert (not math.isnan(k)), "k is nan"
# print("K value is {}".format(k))
#You should trace the update call here, as I recall it leads to the c funtion in a c file
# And not to the python function
## TODO: change S to the correct number (numebr of states until now or what is supposed to be double by k)
# k = k * S
bonus = self.beta_function(A, S, delta, k, Vmax, c)
# The bonus isn't supposed to be the output of beta beta_function
# In the parer it is normilized by k
# TODO: check what should we use as the bonus
if k > 1:
bonus = bonus / k
bonus = bonus / factor_divide_bonus
#print("this is k")
#print (k)
#bonus = 1196518.8710327097
#print("bonus is: {}".format(bonus))
# Rescale or clip immediate reward
reward = self.rescale_reward(self.rescale_reward(reward))
# TODO figure out how to rescale bonus
#bonus = self.rescale_reward(bonus)
total_augmented_reward += reward
ep_t += 1
rewards.append(reward)
states.append(s)
actions.append(a)
bonuses.append(bonus)
max_q_values.append(max_q)
s = new_s
self.local_step += 1
episode_ave_max_q += max_q
global_step, _ = self.global_step.increment()
##We update the target network here
if global_step % self.q_target_update_steps == 0:
self.update_target()
print("We are updating the target networks")
#print("Current k value")
## We update the desity model here
if global_step % self.density_model_update_steps == 0:
#returns the index of the chosen action
self.write_density_model(np.argmax(a))
# Sync local tensorflow target network params with shared target network params
if self.target_update_flags.updated[self.actor_id] == 1:
self.sync_net_with_shared_memory(self.target_network_lower,
self.target_vars_lower)
self.sync_net_with_shared_memory(self.target_network_upper,
self.target_vars_upper)
#TODO: check if needed to duplicate target_update_flags for both nets
self.target_update_flags.updated[self.actor_id] = 0
#print("type of self.density_model_updated: {}".format(type(self.density_model_update_flags)))
for action in range(len(self.density_model_update_flags)):
if self.density_model_update_flags[action].updated[
self.actor_id] == 1:
#returns the index of the chosen action
self.read_density_model(np.argmax(a))
self.density_model_update_flags[action].updated[
self.actor_id] = 0
if self.local_step % self.q_update_interval == 0:
self.batch_update()
self.which_net_to_update_counter += 1
if self.is_master() and (self.local_step % 500 == 0):
#bonus_array = np.array(bonuses)
steps = global_step - global_steps_at_last_record
global_steps_at_last_record = global_step
#logger.debug('Mean Bonus={:.4f} / Max Bonus={:.4f} / STEPS/s={}'.format(
# bonus_array.mean(), bonus_array.max(), steps/float(time.time()-t0)))
t0 = time.time()
else:
#compute monte carlo return
mc_returns = np.zeros((len(rewards), ), dtype=np.float32)
running_total = 0.0
for i, r in enumerate(reversed(rewards)):
running_total = r + self.gamma * running_total
mc_returns[len(rewards) - i - 1] = running_total
mixed_returns = self.cts_eta * np.asarray(rewards) + (
1 - self.cts_eta) * mc_returns
#update replay memory
states.append(new_s)
episode_length = len(rewards)
for i in range(episode_length):
self.replay_memory.append(states[i], actions[i],
mixed_returns[i],
i + 1 == episode_length,
bonuses[i])
#print("Vlow is: {}".format(Vlow))
#print("q_upper values: {}".format(q_values_upper))
#self.q_values_lower_max.append(Vlow)
#self.q_values_lower_max.append(Vhigh)
s, total_episode_reward, _, ep_t, episode_ave_max_q, episode_over = \
self.prepare_state(s, total_episode_reward, self.local_step, ep_t, episode_ave_max_q, episode_over, bonuses, total_augmented_reward, q_values_lower, q_values_upper)
