def main(env_name, num_episodes, render, VideoSave, gamma, lam, kl_targ, batch_size):
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name, render)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime("%b-%d_%H-%M-%S") # create unique directories
logger = Logger(logname=env_name, now=now)
#aigym_path = os.path.join('/tmp', env_name, now)
#env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim, env_name)
scaler.resume()
val_func = NNValueFunction(obs_dim, env_name)
policy = Policy(obs_dim, act_dim, kl_targ, env_name)
episode = 0
capture = False
while episode < num_episodes:
if VideoSave and not capture:
env.ScreenCapture(5)
capture = True
trajectories = run_policy(env, policy, scaler, logger, episodes=batch_size)
episode += len(trajectories)
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, render, gamma, lam, kl_targ, batch_size):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name, render)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime("%b-%d_%H-%M-%S") # create unique directories
logger = Logger(logname=env_name, now=now)
scaler = Scaler(obs_dim, env_name)
val_func = NNValueFunction(obs_dim, env_name)
policy = Policy(obs_dim, act_dim, kl_targ, env_name)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
#capture = False
while episode < num_episodes:
trajectories = run_policy(env, policy, scaler, logger, episodes=batch_size)
episode += len(trajectories)
"""if episode > 600 and not capture:
env.ScreenCapture(5)
capture = True"""
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories, gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger, episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
scaler.save()
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
logger.close()
policy.close_sess()
val_func.close_sess()
class Experiment:
def __init__(self, env_name, discount, num_iterations, lamb, animate, kl_target, show):
self.env_name = env_name
self.env = gym.make(env_name)
if env_name == "FetchReach-v0":
self.env = gym.wrappers.FlattenDictWrapper(self.env, ['observation', 'desired_goal', 'achieved_goal'])
gym.spaces.seed(1234)
self.obs_dim = self.env.observation_space.shape[0]# + 1 # adding time step as feature
self.act_dim = self.env.action_space.shape[0]
self.discount = discount
self.num_iterations = num_iterations
self.lamb = lamb
self.animate = animate
self.episodes = 20
self.killer = GracefulKiller()
# self.policy = ProximalPolicy(self.obs_dim, self.act_dim, self.env.action_space, kl_target, discount=discount,
# lamb=lamb)
self.policy = NoTracePolicy(self.obs_dim, self.act_dim, self.env.action_space, kl_target, epochs=20)
# using MC return would be more helpful
self.value_func = l2TargetValueFunc(self.obs_dim, epochs=10)
# self.value_func = ValueFunc(self.obs_dim, discount=discount, lamb=1)
if not show:
# save copies of file
shutil.copy(inspect.getfile(self.policy.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.value_func.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.__class__), OUTPATH)
self.log_file = open(OUTPATH + 'log.csv', 'w')
self.write_header = True
print('observation dimension:', self.obs_dim)
print('action dimension:', self.act_dim)
# Use of a scaler is crucial
self.scaler = Scaler(self.obs_dim)
self.init_scaler()
def init_scaler(self):
print('fitting scaler')
observation_samples = []
for i in range(5):
observation = []
obs = self.env.reset()
observation.append(obs)
obs = obs.astype(np.float64).reshape((1, -1))
done = False
step = 0
while not done:
# obs = np.append(obs, [[step]], axis=1) # add time step feature
action = self.policy.get_sample(obs).reshape((1, -1)).astype(np.float64)
if self.env_name == "FetchReach-v0":
obs_new, reward, done, _ = self.env.step(action.reshape(-1))
else:
obs_new, reward, done, _ = self.env.step(action)
observation.append(obs_new)
obs = obs_new.astype(np.float64).reshape((1, -1))
step += 1e-3
observation_samples.append(observation)
observation_samples = np.concatenate(observation_samples, axis=0)
# print(observation_samples.shape)
self.scaler.update(observation_samples)
def normalize_obs(self, obs):
scale, offset = self.scaler.get()
obs_scaled = (obs-offset)*scale
self.scaler.update(obs.astype(np.float64).reshape((1, -1)))
return obs_scaled
def run_one_episode(self):
"""
collect data only
:param save:
:param train_policy:
:param train_value_func:
:param animate:
:return:
"""
obs = self.env.reset()
observes, actions, rewards = [],[],[]
done = False
step = 0
while not done:
if self.animate:
self.env.render()
obs = obs.astype(np.float64).reshape((1, -1))
obs = self.normalize_obs(obs)
# obs = np.append(obs, [[step]], axis=1) # add time step feature
observes.append(obs)
action = self.policy.get_sample(obs).reshape((1, -1)).astype(np.float64)
actions.append(action)
if self.env_name == "FetchReach-v0":
obs_new, reward, done, _ = self.env.step(action.reshape(-1))
else:
obs_new, reward, done, _ = self.env.step(action)
if not isinstance(reward, float):
reward = np.asscalar(reward)
rewards.append(reward)
obs = obs_new
step += 0.003
return np.concatenate(observes), np.concatenate(actions), np.array(rewards)
def discounted_sum(self, l, factor):
discounted = []
sum = 0
for i in reversed(l):
discounted.append(factor*sum+i)
sum = factor*sum+i
return np.array(list(reversed(discounted)))
def run_policy(self, episodes):
trajectories = []
for e in range(episodes):
observes, actions, rewards = self.run_one_episode()
trajectory = {'observes': observes,
'actions': actions,
'rewards': rewards}
# scale rewards
if self.discount < 0.999:
rewards = rewards*(1-self.discount)
trajectory['values'] = self.value_func.predict(observes)
trajectory['mc_return'] = self.discounted_sum(rewards, self.discount)
trajectory['td_residual'] = rewards + self.discount*np.append(trajectory['values'][1:],0) - trajectory['values']
trajectory['gae'] = self.discounted_sum(trajectory['td_residual'], self.discount*self.lamb)
trajectories.append(trajectory)
return trajectories
def run_expr(self):
ep_steps = []
ep_rewards = []
ep_entropy = []
i = 0
while i < self.num_iterations:
trajectories = self.run_policy(20)
i += len(trajectories)
observes = np.concatenate([t['observes'] for t in trajectories])
actions = np.concatenate([t['actions'] for t in trajectories])
mc_returns = np.concatenate([t['mc_return'] for t in trajectories])
# advantages = np.concatenate([t['td_residual'] for t in trajectories])
advantages = np.concatenate([t['gae'] for t in trajectories])
# normalize advantage estimates
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-6)
value_func_loss = self.value_func.update(observes, mc_returns)
policy_loss, kl, entropy, beta = self.policy.update(observes, actions, advantages)
avg_rewards = np.sum(np.concatenate([t['rewards'] for t in trajectories])) / self.episodes
avg_timesteps = np.average([len(t['rewards']) for t in trajectories])
log = {}
# compute statistics such as mean and std
log['steps'] = avg_timesteps
log['rewards'] = avg_rewards
log['policy_loss'] = policy_loss
log['kl'] = kl
log['entropy'] = entropy
log['value_func_loss'] = value_func_loss
log['beta'] = beta
# display
print('episode: ', i)
print('average steps: {0}, average rewards: {1}'.format(log['steps'], log['rewards']))
for key in ['policy_loss', 'kl', 'entropy', 'beta', 'value_func_loss']:
print('{:s}: {:.2g}'.format(key, log[key]))
print('\n')
ep_steps.append(log['steps'])
ep_rewards.append(log['rewards'])
ep_entropy.append(log['entropy'])
# write to log.csv
if self.write_header:
fieldnames = [x for x in log.keys()]
self.writer = csv.DictWriter(self.log_file, fieldnames=fieldnames)
self.writer.writeheader()
self.write_header = False
self.writer.writerow(log)
# we want the csv file to preserve information even if the program terminates earlier than scheduled.
self.log_file.flush()
# save model weights if stopped manually
if self.killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
self.killer.kill_now = False
# if (i+1)%20 == 0:
# print('episode: ', i+1)
# print('average steps', np.average(steps))
# print('average rewards', np.average(rewards))
self.policy.save(OUTPATH)
self.value_func.save(OUTPATH)
self.scaler.save(OUTPATH)
plt.figure(figsize=(12,9))
if self.env_name.startswith('Fetch'):
ax1 = plt.subplot(121)
plt.xlabel('episodes')
plt.ylabel('policy entropy')
plt.plot(ep_entropy)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * scale_x))
ax1.xaxis.set_major_formatter(ticks_x)
else:
ax1 = plt.subplot(121)
plt.xlabel('episodes')
plt.ylabel('steps')
plt.plot(ep_steps)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * scale_x))
ax1.xaxis.set_major_formatter(ticks_x)
ax2 = plt.subplot(122)
plt.xlabel('episodes')
plt.ylabel('episodic rewards')
plt.plot(ep_rewards)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * scale_x))
ax2.xaxis.set_major_formatter(ticks_x)
plt.savefig(OUTPATH + 'train.png')
def load_model(self, load_from):
from tensorflow.python.tools import inspect_checkpoint as chkp
# # print all tensors in checkpoint file
# chkp.print_tensors_in_checkpoint_file(load_from+'policy/policy.pl', tensor_name='', all_tensors=True, all_tensor_names=True)
self.policy.load(load_from + 'policy/policy.pl')
self.value_func.load(load_from + 'value_func/value_func.pl')
def demonstrate_agent(self, load_from):
self.load_model(load_from)
with open(load_from + "scaler.pkl", 'rb') as file:
self.scaler = pickle.load(file)
self.animate = True
for i in range(10):
observes, actons, rewards = self.run_one_episode()
ep_rewards = np.sum(rewards)
ep_steps = len(rewards)
print("Total steps: {0}, total rewards: {1}\n".format(ep_steps, ep_rewards))
class GeneratorAgentPure(object):
def __init__(self,
env,
policy_function,
value_function,
discriminator,
gamma,
lam,
init_qpos,
init_qvel,
logger=None):
self.env = env
self.obs_dim = env.observation_space.shape[0]
self.act_dim = env.action_space.shape[0]
self.policy = policy_function
self.value = value_function
self.discriminator = discriminator
self.gamma = gamma
self.lam = lam
self.init_qpos = init_qpos
self.init_qvel = init_qvel
self.scaler = Scaler(self.obs_dim)
# logger
self.logger = logger
# set scaler's scale and offset by collecting 5 episodes
self.collect(timesteps=2048)
def discount(self, x, gamma):
return scipy.signal.lfilter([1.0], [1.0, -gamma], x[::-1])[::-1]
def get_random(self):
idx = np.random.randint(low=0, high=self.init_qpos.shape[1], size=1)
return np.squeeze(self.init_qpos[:,
idx]), np.squeeze(self.init_qvel[:,
idx])
def collect(self, timesteps):
trajectories = []
trew_stat = []
scale, offset = self.scaler.get()
self.logger.log('scale_offset', [scale, offset])
buffer_time = 0
while buffer_time < timesteps:
unscaled_obs, scaled_obs, actions, rewards = [], [], [], []
egocentric = []
done = False
obs = self.env.reset()
qpos, qvel = self.get_random()
# we are setting initial qpos and qvel from expert
self.env.set_state(qpos, qvel)
timestep = 0
while not done and timestep < 1000:
obs = obs.astype(np.float32).reshape(1, -1)
unscaled_obs.append(obs)
obs = (obs - offset) * scale
scaled_obs.append(obs)
acts = self.policy.sample(obs)
actions.append(acts.astype(np.float32).reshape(1, -1))
obs, rew, done, _ = self.env.step(acts)
rewards.append(rew)
timestep += 1
buffer_time += 1
# statistics
trew_stat.append(np.sum(rewards))
# episode info
traj_obs = np.concatenate(scaled_obs)
traj_unscaled_obs = np.concatenate(unscaled_obs)
traj_acts = np.concatenate(actions)
#traj_rews = np.array(rewards, dtype=np.float64)
traj_rews = np.squeeze(
self.discriminator.get_rewards(traj_unscaled_obs, traj_acts))
# scale rewards using running std of the experiment
# traj_scaled_rews = traj_rews * np.squeeze(rew_scale)
traj_scaled_rews = traj_rews
# calculate discount sum of rewards
traj_disc_rews = self.discount(traj_scaled_rews, self.gamma)
# calculate advantages
traj_values = self.value.predict(traj_obs)
deltas = traj_scaled_rews - traj_values + np.append(
traj_values[1:] * self.gamma, 0)
traj_advantages = self.discount(deltas, self.gamma * self.lam)
trajectory = {
'observations': traj_obs,
'actions': traj_acts,
'tdlam': traj_disc_rews,
'advantages': traj_advantages,
'unscaled_obs': traj_unscaled_obs
}
trajectories.append(trajectory)
# update observation scaler
uns_obs = np.concatenate([t['unscaled_obs'] for t in trajectories])
self.scaler.update(uns_obs)
# update rewards scaler
#uns_rews = np.concatenate([t['unscaled_rews'] for t in trajectories])
#self.rew_scaler.update(uns_rews)
observations = np.concatenate(
[t['observations'] for t in trajectories])
actions = np.concatenate([t['actions'] for t in trajectories])
tdlam = np.concatenate([t['tdlam'] for t in trajectories])
advantages = np.concatenate([t['advantages'] for t in trajectories])
advantages = (advantages - np.mean(advantages)) / np.std(advantages)
# check stats
print('mean_trew: %f' % np.mean(trew_stat))
self.logger.log('trew_stat', np.mean(trew_stat))
return observations, uns_obs, actions, tdlam, advantages
def train_models(env_name, num_episodes, gamma, lam, kl_targ, coef,
use_lr_adjust, ada_kl_penalty, seed, epochs, phi_epochs,
max_timesteps, reg_scale, phi_lr, phi_hs, policy_size,
phi_obj, load_model):
env, obs_dim, act_dim = init_gym(env_name)
set_global_seeds(seed)
env.seed(seed)
env._max_episode_steps = max_timesteps
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
aigym_path = os.path.join('log-files/', env_name, now)
env = wrappers.Monitor(env, aigym_path, force=True, video_callable=False)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim)
policy = Policy(obs_dim,
act_dim,
kl_targ,
epochs,
phi_epochs,
policy_size=policy_size,
phi_hidden_sizes=phi_hs,
reg_scale=reg_scale,
lr_phi=phi_lr,
phi_obj=phi_obj)
run_policy(env,
policy,
scaler,
num_episodes,
max_timesteps=max_timesteps,
mode=load_model) # run a few to init scaler
episode = 0
for i in range(2000):
print("sampling and training at %s iteration\n" % (i))
trajectories, traj_len_list = run_policy(env,
policy,
scaler,
num_episodes,
max_timesteps=max_timesteps,
mode=load_model)
num_traj = len(trajectories)
episode += len(trajectories)
add_value(trajectories, val_func)
add_disc_sum_rew(trajectories, gamma)
add_gae(trajectories, gamma, lam)
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
policy.update(load_model,
observes,
actions,
advantages,
use_lr_adjust,
ada_kl_penalty,
c=0.) # update policy
val_func.fit(observes, disc_sum_rew)
# Save models
policy.save_policy()
val_func.save_val_func()
refine_scaler = False
if refine_scaler == True:
run_policy(env,
policy,
scaler,
num_episodes,
max_timesteps=max_timesteps,
mode=load_model) # run a few to refine scaler
with open('models/scaler/scaler.pkl', 'wb') as output:
pickle.dump(scaler, output, pickle.HIGHEST_PROTOCOL)
logger.log("saved model")
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
#env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(env_name, obs_dim, act_dim, kl_targ, hid1_mult,
policy_logvar)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
scale, offset = scaler.get()
data = {'SCALE': scale, 'OFFSET': offset}
directory_to_store_data = '../saved_models/' + env_name + '/'
if not os.path.exists(directory_to_store_data):
os.makedirs(directory_to_store_data)
file_name = directory_to_store_data + 'scale_and_offset.pkl'
with open(file_name, 'wb') as f:
pickle.dump(data, f)
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
"""
killer = GracefulKiller()
#TODO Change init_gym for one of my functions
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime("%b-%d_%H:%M:%S").replace(
":", "_") # create unique directories
logger = Logger(logname=env_name, now=now)
pathFolder = logger.pathFolder
#Change wrappers.Monitor for a class of mine that controls de simulation
#Creo que el wrapper no sirve de nada para mi ejemplo
#env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim)
policy = Policy(obs_dim, act_dim, kl_targ)
#Esto es para alimentar con el optimo
trajectories = initiatePolicyWithOptimum(env, policy, scaler, logger)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories, gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(trajectories)
print(actions.shape)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
# No estoy seguro de si esto es necesario ya
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
logger.close()
policy.close_sess(pathFolder)
val_func.close_sess(pathFolder)
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar, max_frames, load_and_run):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
print("Env loaded.")
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
# env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, policy_logvar)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
if load_and_run:
val_func.load_weights()
while True:
run_episode(env, policy, scaler, animate=True)
exit()
episode = 0
print("Running episodes...")
while episode < num_episodes:
episode_startime = time.time()
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size,
max_frames=max_frames)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
try:
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
except Exception as e:
print(e)
print('skipping...')
continue
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
print("Batch took %i seconds to run." %
(time.time() - episode_startime))
if not episode % 1000:
val_func.save_weights()
run_episode(env, policy, scaler, animate=True)
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size,
net_size_factor, noise_bias, weight, use_ppoclip):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
# now = datetime.utcnow().strftime("%b-%d_%H:%M:%S") # create unique directories
now = datetime.now().strftime("%b-%d_%H:%M:%S") + "_single"
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
# env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
if weight == "None":
val_func = NNValueFunction(obs_dim, net_size_factor=net_size_factor)
policy = None
if use_ppoclip == "False":
policy = Policy(obs_dim,
act_dim,
kl_targ,
net_size_factor=net_size_factor,
noise_bias=noise_bias)
elif use_ppoclip == "True":
policy = PolicyClip(obs_dim,
act_dim,
kl_targ,
net_size_factor=net_size_factor,
noise_bias=noise_bias)
#assert False, "Not tested"
else:
assert False, "Unreachable"
else:
token = weight.split(".")
token[-3] = token[-3][:-5] + "value"
weight_2 = ".".join(token)
val_func = NNValueFunctionContinue(weight_2,
obs_dim,
net_size_factor=net_size_factor)
policy = PolicyContinue(weight,
obs_dim,
act_dim,
kl_targ,
net_size_factor=net_size_factor,
noise_bias=noise_bias)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger,
scaler) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
logger.close()
# with open("test_dump", 'w') as f:
# pickle.dump(policy, f)
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size,hid1_mult,
policy_logvar, weights_path, init_episode, experiment_name, resume, augment=False):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
killer = GracefulKiller()
logger = Logger(logname=env_name, sub_dir=experiment_name)
aigym_path = os.path.join('results', env_name, experiment_name)
if resume:
weights_path = aigym_path
ckpt = tf.train.get_checkpoint_state(weights_path)
init_episode = int(os.path.basename(ckpt.model_checkpoint_path).split('-')[1])
env, obs_dim, act_dim = init_gym(env_name)
obs_dim = 45
# obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
# env = wrappers.Monitor(env, aigym_path, force=True)
if augment:
obs_dim *= 2
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, policy_logvar, weights_path)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, 5, augment)
episode = init_episode
while episode <= num_episodes:
if episode % 1000 is 0:
# record one episode
record(env_name, aigym_path, policy, scaler, augment)
policy.save(aigym_path, episode)
trajectories = run_policy(env, policy, scaler, logger, batch_size, augment)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories, gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger, episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
#record one last episode
record(env_name, aigym_path, policy, scaler, augment)
logger.close()
policy.close_sess()
val_func.close_sess()
gamma = 0.995
lam = 0.98
batch_size = 5
env = gym.make(env_name)
obs_dim = env.observation_space.shape[0]
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
act_dim = env.action_space.shape[0]
# sess = tf.Session()
policy = Policy(obs_dim, act_dim)
val_func = NNValueFunction(obs_dim)
# sess.run(tf.compat.v1.initializers.global_variables())
now = datetime.utcnow().strftime("%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
scaler = Scaler(obs_dim)
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
while episode < num_episodes:
trajectories = run_policy(env, policy, scaler, logger, episodes=batch_size)
episode += len(trajectories)
observes, actions, advantages, disc_sum_rew = build_train_set(trajectories, val_func, gamma, lam)
policy.update(observes, actions, advantages, logger)
val_func.fit(observes, disc_sum_rew, logger)
logger.log({
'_Episode': episode,
})
logger.write(display=True)
class Experiment:
def __init__(self, discount, num_iterations, lamb, animate, kl_target, **kwargs):
self.env_name = 'RoboschoolHumanoidFlagrun-v1'
self.env = gym.make(self.env_name)
gym.spaces.seed(1234) # for reproducibility
self.obs_dim = self.env.observation_space.shape[0] + 1 # adding time step as feature
self.act_dim = self.env.action_space.shape[0]
self.discount = discount
self.num_iterations = num_iterations
self.lamb = lamb
self.animate = animate
self.buffer = Buffer(1000000, self.obs_dim, self.act_dim) # 1000000 is the size they have used in paper
self.episodes = 20 # larger episodes can reduce variance
self.killer = GracefulKiller()
self.policy = QPropPolicy(self.obs_dim, self.act_dim, self.env.action_space, kl_target, epochs=20)
self.critic = DeterministicCritic(self.obs_dim, self.act_dim, self.discount, OUTPATH)
self.value_func = l2TargetValueFunc(self.obs_dim, epochs=10)
if 'show' in kwargs and not kwargs['show']:
# save copies of file
shutil.copy(inspect.getfile(self.policy.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.value_func.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.critic.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.__class__), OUTPATH)
self.log_file = open(OUTPATH + 'log.csv', 'w')
self.write_header = True
print('Observation dimension:', self.obs_dim)
print('Action dimension:', self.act_dim)
# The use of a scaler is crucial
self.scaler = Scaler(self.obs_dim)
self.init_scaler()
def init_scaler(self):
"""
Collection observations from 5 episodes to initialize Scaler.
:return: a properly initialized scaler
"""
print('Fitting scaler')
observation_samples = []
for i in range(5):
observation = []
obs = self.env.reset()
observation.append(obs)
obs = obs.astype(np.float64).reshape((1, -1))
done = False
step = 0
while not done:
obs = np.append(obs, [[step]], axis=1) # add time step feature
action = self.policy.get_sample(obs).reshape((1, -1)).astype(np.float64)
obs_new, reward, done, _ = self.env.step(action.reshape(-1))
observation.append(obs_new)
obs = obs_new.astype(np.float64).reshape((1, -1))
step += 1e-3
observation_samples.append(observation)
observation_samples = np.concatenate(observation_samples, axis=0)
self.scaler.update(observation_samples)
def normalize_obs(self, obs):
"""
Transform and update the scaler on the fly.
:param obs: Raw observation
:return: normalized observation
"""
scale, offset = self.scaler.get()
obs_scaled = (obs-offset)*scale
self.scaler.update(obs.astype(np.float64).reshape((1, -1)))
return obs_scaled
def run_one_episode(self):
"""
collect a trajectory of (obs, act, reward, obs_next)
"""
obs = self.env.reset()
observes, actions, rewards = [],[],[]
done = False
step = 0
while not done:
if self.animate:
self.env.render()
obs = obs.astype(np.float64).reshape((1, -1))
obs = self.normalize_obs(obs)
obs = np.append(obs, [[step]], axis=1) # add time step feature at normalized observation
observes.append(obs)
action = self.policy.get_sample(obs).reshape((1, -1)).astype(np.float64)
actions.append(action)
obs_new, reward, done, _ = self.env.step(action.reshape(-1))
if not isinstance(reward, float):
reward = np.asscalar(reward)
rewards.append(reward)
obs = obs_new
step += 0.003
return np.concatenate(observes), np.concatenate(actions), np.array(rewards)
def discounted_sum(self, l, factor):
"""
Discounted sum of return or advantage estimates along a trajectory.
:param l: a list containing the values of discounted summed interest.
:param factor: discount factor in the disc_sum case or discount*lambda for GAE
:return: discounted sum of l with regard to factor
"""
discounted = []
sum = 0
for i in reversed(l):
discounted.append(factor*sum+i)
sum = factor*sum+i
return np.array(list(reversed(discounted)))
def run_policy(self, episodes):
"""
Gather a batch of trajectory samples.
:param episodes: size of batch.
:return: a batch of samples
"""
trajectories = []
for e in range(episodes):
observes, actions, rewards = self.run_one_episode()
trajectory = {'observes': observes,
'actions': actions,
'rewards': rewards,
'scaled_rewards': rewards*(1-self.discount)}
trajectories.append(trajectory)
return trajectories
def run_expr(self):
ep_steps = []
ep_rewards = []
ep_entropy = []
i = 0
while i < self.num_iterations:
trajectories = self.run_policy(20)
# add to experience replay buffer
self.buffer.append(trajectories)
print('buffer size:', self.buffer.size())
i += len(trajectories)
# for E=20, T=50, the total number of samples would be 1000
# In future needs to account for not uniform time steps per episode.
# e.g. in Hopper-v2 environment not every episode has same time steps
# E = len(trajectories)
# num_samples = np.sum([len(t['rewards']) for t in trajectories])
gradient_steps = np.sum([len(t['rewards']) for t in trajectories])
"""train critic"""
# train all samples in the buffer, to the extreme
# self.critic.fit(self.policy, self.buffer, epochs=20, num_samples=self.buffer.size())
# train some samples minibatches only
critic_loss_mean, critic_loss_std = self.critic.another_fit_func(self.policy, self.buffer, gradient_steps)
"""calculation of episodic discounted return only needs rewards"""
mc_returns = np.concatenate([self.discounted_sum(t['scaled_rewards'], self.discount) for t in trajectories])
"""using current batch of samples to update baseline"""
observes = np.concatenate([t['observes'] for t in trajectories])
actions = np.concatenate([t['actions'] for t in trajectories])
value_func_loss = self.value_func.update(observes, mc_returns)
"""compute GAE"""
for t in trajectories:
t['values'] = self.value_func.predict(t['observes'])
# IS it really legitimate to insert 0 at the last obs?
t['td_residual'] = t['scaled_rewards'] + self.discount * np.append(t['values'][1:], 0) - t['values']
t['gae'] = self.discounted_sum(t['td_residual'], self.discount * self.lamb)
advantages = np.concatenate([t['gae'] for t in trajectories])
"""normalize advantage estimates, Crucial step"""
advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-6)
"""compute control variate"""""
cv = self.critic.get_contorl_variate(self.policy, observes, actions)
# cv must not be centered
# cv = (cv - cv.mean()) / (cv.std() + 1e-6)
"""conservative control variate"""
eta = [1 if i > 0 else 0 for i in advantages*cv]
"""center learning signal"""
# check that advantages and CV should be of size E*T
# eta controls the on-off of control variate
learning_signal = advantages - eta*cv
# learning_signal = (learning_signal - learning_signal.mean()) / (learning_signal.std() + 1e-6)
"""controlled taylor eval term"""
ctrl_taylor = np.concatenate([ [eta[i]*act] for i, act in enumerate(self.critic.get_taylor_eval(self.policy, observes))])
"""policy update"""
ppo_loss, ddpg_loss, kl, entropy, beta = self.policy.update(observes, actions, learning_signal, ctrl_taylor)
avg_rewards = np.sum(np.concatenate([t['rewards'] for t in trajectories])) / self.episodes
avg_timesteps = np.average([len(t['rewards']) for t in trajectories])
log = {}
# save training statistics
log['steps'] = avg_timesteps
log['rewards'] = avg_rewards
log['critic_loss'] = critic_loss_mean
log['policy_ppo_loss'] = ppo_loss
log['policy_ddpg_loss'] = ddpg_loss
log['kl'] = kl
log['entropy'] = entropy
log['value_func_loss'] = value_func_loss
log['beta'] = beta
# display
print('episode: ', i)
print('average steps: {0}, average rewards: {1}'.format(log['steps'], log['rewards']))
for key in ['critic_loss', 'policy_ppo_loss', 'policy_ddpg_loss', 'value_func_loss', 'kl', 'entropy', 'beta']:
print('{:s}: {:.2g}'.format(key, log[key]))
print('\n')
ep_steps.append(log['steps'])
ep_rewards.append(log['rewards'])
ep_entropy.append(log['entropy'])
# write to log.csv
if self.write_header:
fieldnames = [x for x in log.keys()]
self.writer = csv.DictWriter(self.log_file, fieldnames=fieldnames)
self.writer.writeheader()
self.write_header = False
self.writer.writerow(log)
# we want the csv file to preserve information even if the program terminates earlier than scheduled.
self.log_file.flush()
# save model weights if stopped early
if self.killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
self.killer.kill_now = False
self.policy.save(OUTPATH)
self.value_func.save(OUTPATH)
self.critic.save(OUTPATH)
self.scaler.save(OUTPATH)
plt.figure(figsize=(12,9))
if self.env_name.startswith('Fetch'):
ax1 = plt.subplot(121)
plt.xlabel('episodes')
plt.ylabel('policy entropy')
plt.plot(ep_entropy)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * scale_x))
ax1.xaxis.set_major_formatter(ticks_x)
else:
ax1 = plt.subplot(121)
plt.xlabel('episodes')
plt.ylabel('steps')
plt.plot(ep_steps)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * scale_x))
ax1.xaxis.set_major_formatter(ticks_x)
ax2 = plt.subplot(122)
plt.xlabel('episodes')
plt.ylabel('episodic rewards')
plt.plot(ep_rewards)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x * scale_x))
ax2.xaxis.set_major_formatter(ticks_x)
plt.savefig(OUTPATH + 'train.png')
def load_model(self, load_from):
"""
Load all Function Approximators plus a Scaler.
Replaybuffer is not restored though.
:param load_from: Dir containing saved weights.
"""
from tensorflow.python.tools import inspect_checkpoint as chkp
# # print all tensors in checkpoint file
# chkp.print_tensors_in_checkpoint_file(load_from+'policy/policy.pl', tensor_name='', all_tensors=True, all_tensor_names=True)
self.policy.load(load_from + 'policy/')
self.value_func.load(load_from + 'value_func/')
self.critic.load(load_from+'critic/')
with open(load_from + "scaler.pkl", 'rb') as file:
self.scaler = pickle.load(file)
def demonstrate_agent(self, load_from):
"""
Simply run the policy without training.
:param load_from:
:return:
"""
self.load_model(load_from)
while True:
observes, actons, rewards = self.run_one_episode()
ep_rewards = np.sum(rewards)
ep_steps = len(rewards)
print("Total steps: {0}, total rewards: {1}\n".format(ep_steps, ep_rewards))
class Agent:
def __init__(self, env, name, chief=None):
assert name == 'chief' or 'worker' in name
if 'worker' in name:
assert chief is not None
self.chief = chief
else:
self.scaler = Scaler(Config.data.state_dim)
self.name = name
self.env = env
self.sess = None
self.coord = None
with tf.variable_scope(name):
self._build_graph()
def _build_graph(self):
self.actor = Actor()
self.critic = Critic()
if 'worker' in self.name:
self._build_update_op()
def _build_update_op(self):
global_step = tf.train.get_global_step()
tf.assign_add(global_step, 1, name='global_step_add')
with tf.variable_scope('sync'):
with tf.variable_scope('pull'):
pull_a_params_op = [
actor_param.assign(chief_param)
for actor_param, chief_param in zip(
self.actor.params, self.chief.actor.params)
]
pull_c_params_op = [
critic_param.assign(chief_param)
for critic_param, chief_param in zip(
self.critic.params, self.chief.critic.params)
]
self.pull_op = tf.group(pull_a_params_op + pull_c_params_op)
with tf.variable_scope('push'):
update_a_op = self.chief.actor.optimizer.apply_gradients(
zip(self.actor.grads, self.chief.actor.params))
update_c_op = self.chief.critic.optimizer.apply_gradients(
zip(self.critic.grads, self.chief.critic.params))
self.update_op = tf.group([update_a_op, update_c_op])
def init_scaler(self, init_episode=5):
for e in range(init_episode):
observation = self.env.reset()
states = []
done = False
count = 0
while not done:
states.append(observation)
action = self.choose_action(observation)
next_observation, reward, done, info = self.env.step(action)
observation = next_observation
if Config.train.get('max_episode_steps', None):
count += 1
if count == Config.train.max_episode_steps:
break
self.scaler.update(np.array(states))
def update_chief(self, states, actions, target_v):
feed_dict = {self.critic.states: states}
value = self.sess.run(self.critic.value, feed_dict)
td_error = np.array(target_v) - value
feed_dict = {
self.critic.states: states,
self.critic.target_v: target_v,
self.actor.states: states,
self.actor.actions: actions,
self.actor.td_error: td_error
}
self.sess.run([
self.critic.loss, self.update_op, self.name + '/global_step_add:0'
], feed_dict)
def pull_params(self):
self.sess.run(self.pull_op)
def cal_target_v(self, done, next_observation, rewards):
if done:
next_value = 0
else:
next_value = self.sess.run(
self.critic.value, {
self.critic.states:
[self.chief.scaler.normalize(next_observation)]
})[0, 0]
target_v = []
for reward in rewards[::-1]:
next_value = reward + Config.train.reward_decay * next_value
target_v.append([next_value])
target_v.reverse()
return target_v
def choose_action(self, observation):
if Config.data.action_type == 'discrete':
policy = self.sess.run(self.actor.policy,
{self.actor.states: [observation]})[0]
action = np.random.choice(range(Config.data.action_num), p=policy)
else:
action = self.sess.run(self.actor.sample,
{self.actor.states: [observation]})
return action
def eval(self, animate=False):
assert self.name == 'chief'
observation = self.env.reset()
ep_reward = 0
count = 0
done = False
while not done:
if animate:
self.env.render()
action = self.choose_action(self.scaler.normalize(observation))
next_observation, reward, done, info = self.env.step(action)
ep_reward += reward
observation = next_observation
if Config.train.get('max_episode_steps', None):
count += 1
if count == Config.train.max_episode_steps:
break
return ep_reward
def work(self):
total_step = 0
states, actions, rewards, unscaled_states = [], [], [], []
self.pull_params()
while not self.coord.should_stop():
observation = self.env.reset()
ep_reward = 0
done = False
count = 0
while not done:
unscaled_states.append(observation)
observation = self.chief.scaler.normalize(observation)
states.append(observation)
action = self.choose_action(observation)
next_observation, reward, done, info = self.env.step(action)
total_step += 1
ep_reward += reward
actions.append(action)
rewards.append(reward)
if total_step % Config.train.update_n_iter == 0 or done:
target_v = self.cal_target_v(done, next_observation,
rewards)
self.update_chief(states, actions, target_v)
self.chief.scaler.update(np.array(unscaled_states))
states, actions, rewards, unscaled_states = [], [], [], []
self.pull_params()
observation = next_observation
if Config.train.get('max_episode_steps', None):
count += 1
if count == Config.train.max_episode_steps:
break
def main2(env_name, num_episodes, gamma, lam, kl_targ, batch_size,
net_size_factor, noise_bias, weight, use_ppoclip):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
"""
global alive_coef, progress_coef, threshold1, threshold2, change_rate
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
# now = datetime.utcnow().strftime("%b-%d_%H:%M:%S") # create unique directories
now = datetime.now().strftime(
"%b-%d_%H:%M:%S") + "_multi_hop_{},{},{}".format(
change_rate, threshold1, threshold2)
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
# env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
if weight == "None":
val_func = NNValueFunction(obs_dim,
net_size_factor=net_size_factor,
alive_coef=alive_coef,
progress_coef=progress_coef,
reward_dim=reward_dim)
policy = Policy(obs_dim,
act_dim,
kl_targ,
net_size_factor=net_size_factor,
noise_bias=noise_bias)
else:
token = weight.split(".")
token[-3] = token[-3][:-5] + "value"
weight_2 = ".".join(token)
# assert False, "unreachable"
val_func = NNValueFunctionContinue(weight_2,
obs_dim,
net_size_factor=net_size_factor,
alive_coef=alive_coef,
progress_coef=progress_coef)
policy = PolicyContinue(weight,
obs_dim,
act_dim,
kl_targ,
net_size_factor=net_size_factor,
noise_bias=noise_bias)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
flag1 = False
flag2 = False
flag3 = False
reward_queue = []
queue_num = 100
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger,
scaler) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
alive_sum = 0
progr_sum = 0
for t in trajectories:
tmp_rewards = t['orig_rewards']
tmp_rewards = np.sum(tmp_rewards, axis=0)
alive_sum += tmp_rewards[0]
progr_sum += tmp_rewards[1]
reward_queue.append(np.mean([t['rewards'].sum()
for t in trajectories]))
reward_queue = reward_queue[-queue_num:]
reward_std = np.std(np.array(reward_queue))
print("Reward std by {} episode : {}".format(queue_num, reward_std))
if alive_sum >= 5000:
flag3 = True
if (flag3 and alive_sum > progr_sum * threshold1) or flag1:
flag1 = True
alive_coef -= change_rate
progress_coef += change_rate
val_func.alive_coef = float(alive_coef)
val_func.progress_coef = float(progress_coef)
if alive_sum < progr_sum * threshold2:
flag1 = False
if progr_sum > alive_sum * threshold1 or flag2:
flag2 = True
alive_coef += change_rate
progress_coef -= change_rate
val_func.alive_coef = float(alive_coef)
val_func.progress_coef = float(progress_coef)
if progr_sum < alive_sum * threshold2:
flag2 = False
print(alive_sum, progr_sum)
logger.log_model_3({
"alive_coef": alive_coef,
"progress_coef": progress_coef,
"alive_sum": alive_sum,
"progr_sum": progr_sum
})
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar):
'''
'''
##################
# shared policy #
##################
tic = time.clock()
manarger = MPManager()
manarger.start()
shared_env, shared_obs_dim, shared_act_dim = init_gym(env_name)
shared_obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
shared_logger = Logger(logname=env_name, now=now + "-Master")
shared_aigym_path = os.path.join('./vedio', env_name, now + "-Master")
#env = wrappers.Monitor(env, aigym_path, force=True)
shared_scaler = Scaler(shared_obs_dim)
shared_val_func = NNValueFunction(shared_obs_dim, hid1_mult, -1, None)
shared_policy = Policy(shared_obs_dim, shared_act_dim, kl_targ, hid1_mult,
policy_logvar, -1, None)
learning_rate_input = tf.placeholder("float")
grad_applier = RMSPropApplier(learning_rate=learning_rate_input,
decay=RMSP_ALPHA,
momentum=0.0,
epsilon=RMSP_EPSILON,
clip_norm=GRAD_NORM_CLIP,
device=device)
# lacal policy declair
env_a = [None] * N_WORKERS
obs_dim_a = [None] * N_WORKERS
act_dim_a = [None] * N_WORKERS
logger_a = [None] * N_WORKERS
aigym_path_a = [None] * N_WORKERS
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
val_func_a = [None] * N_WORKERS
policy_a = [None] * N_WORKERS
scaler_a = [None] * N_WORKERS
for i in range(N_WORKERS):
env_a[i], obs_dim_a[i], act_dim_a[i] = init_gym(env_name)
obs_dim_a[
i] += 1 # add 1 to obs dimension for time step feature (see run_episode())
logger_a[i] = Logger(logname=env_name, now=now + "-" + str(i))
aigym_path_a[i] = os.path.join('./vedio', env_name, now + "-" + str(i))
#env_a[i] = wrappers.Monitor(env, aigym_path, force=True)
scaler_a[i] = Scaler(obs_dim_a[i])
val_func_a[i] = NNValueFunction(obs_dim_a[i], hid1_mult, i,
shared_val_func)
val_func_a[i].apply_gradients = grad_applier.apply_gradients(
shared_val_func.get_vars(), val_func_a[i].gradients)
policy_a[i] = Policy(obs_dim_a[i], act_dim_a[i], kl_targ, hid1_mult,
policy_logvar, i, shared_policy)
policy_a[i].apply_gradients = grad_applier.apply_gradients(
shared_policy.get_vars(), policy_a[i].gradients)
# init tensorflow
sess = tf.Session(config=tf.ConfigProto(log_device_placement=False,
allow_soft_placement=True))
init = tf.global_variables_initializer()
## start sess
sess.run(init)
## init shared scalar policy
run_policy(sess,
shared_env,
shared_policy,
shared_scaler,
shared_logger,
episodes=5)
def single_work(thread_idx):
""" training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
env = env_a[thread_idx]
policy = policy_a[thread_idx]
#obs_dim = obs_dim_a[thread_idx]
#act_dim = act_dim_a[thread_idx]
logger = logger_a[thread_idx]
aigym_path = aigym_path_a[thread_idx]
scaler = scaler_a[thread_idx]
val_func = val_func_a[thread_idx]
print("=== start thread " + str(policy.get_thread_idx()) + " " +
policy.get_scope() + " ===")
print(shared_policy.get_vars())
print(policy.get_vars())
# run a few episodes of untrained policy to initialize scaler:
#run_policy(sess, env, policy, scaler, logger, episodes=5)
#policy.sync(shared_policy)
#val_func.sync(shared_val_func)
episode = 0
while episode < num_episodes:
## copy global var into local
sess.run(policy.sync)
sess.run(val_func.sync)
## compute new model on local policy
trajectories = run_policy(sess,
env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(sess, trajectories,
val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew,
logger, episode,
time.clock() - tic)
policy.update(sess, observes, actions, advantages,
logger) # update policy
val_func.fit(sess, observes, disc_sum_rew,
logger) # update value function
#cur_learning_rate = self._anneal_learning_rate(global_t)
feed_dict = {
policy.old_log_vars_ph: policy.old_log_vars_np,
policy.old_means_ph: policy.old_means_np,
policy.obs_ph: observes,
policy.act_ph: actions,
policy.advantages_ph: advantages,
policy.beta_ph: policy.beta,
policy.lr_ph: policy.lr,
policy.eta_ph: policy.eta,
learning_rate_input: policy.lr
}
sess.run(policy.apply_gradients, feed_dict)
shared_policy.update(sess, observes, actions, advantages,
shared_logger)
feed_dict = {
val_func.obs_ph: observes,
val_func.val_ph: disc_sum_rew,
learning_rate_input: val_func.lr
}
sess.run(val_func.apply_gradients, feed_dict)
shared_val_func.fit(sess, observes, disc_sum_rew, shared_logger)
shared_logger.log({'_Time': time.clock() - tic})
logger.write(
display=True) # write logger results to file and stdout
logger.close()
## end def single work
train_threads = []
for i in range(N_WORKERS):
train_threads.append(threading.Thread(target=single_work, args=(i, )))
[t.start() for t in train_threads]
[t.join() for t in train_threads]
saver = tf.train.Saver()
for i in range(N_WORKERS):
logger_a[i].close()
#path = os.path.join('log-files', env_name, now+'-Master', 'checkpoint')
#saver.save(sess, path )
sess.close()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar, task_identity):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
print('Training started for ' + env_name + ' and task_identity ' +
str(task_identity))
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name=env_name,
task_identity=task_identity)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, policy_logvar,
env_name, task_identity)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
scale, offset = scaler.get()
#scale_and_offset_data = {'scale': scale, 'offset': offset}
#scale_and_offset_file = 'scale_and_offset_file_' + env_name + '_' + task_identity + '.pkl'
#with open(scale_and_offset_file, 'wb') as f:
# pickle.dump(scale_and_offset_data, f)
#### Saving expert trajectories after sufficient training has been made
## Visualization
#aigym_path = os.path.join(VIDEO_LOGS_DIRECTORY, env_name, now)
#env = wrappers.Monitor(env, aigym_path, force=True)
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=DEMONSTRATOR_EPISODES_TO_LOG)
data_to_store = {
DEMONSTRATOR_TRAJECTORY_KEY: trajectories,
SCALE_KEY: scale,
OFFSET_KEY: offset
}
directory_to_store_trajectories = './../' + DEMONSTRATOR_TRAJECTORIES_DIRECTORY
if not os.path.exists(directory_to_store_trajectories):
os.makedirs(directory_to_store_trajectories)
file_to_store_trajectories = directory_to_store_trajectories + env_name + '_' + task_identity + '.pkl'
with open(file_to_store_trajectories, "wb") as f:
pickle.dump(data_to_store, f)
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult, init_logvar):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (multiplier of obs dimension)
init_logvar: natural log of initial policy variance
"""
print('load model (l)?')
loading = input('')
pybullet.connect(pybullet.DIRECT)
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
# print('obs_dim') # 45 for HumanoidFlagrunBulletEnv-v0, HumanoidFlagrunHarderBulletEnv-v0
# print(obs_dim)
# print('act_dim') # 17 for HumanoidFlagrunBelletEnv-v0, HumanoidFlagrunHarderBulletEnv-v0
# print(act_dim)
# input('')
now = datetime.utcnow().strftime("%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult, loading)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, init_logvar)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
policy_model = policy.get_trpo_policy_model()
valNN_model = val_func.get_valNN_model()
lr = val_func.get_lr()
if loading == 'l':
policy_model.load_weights('pol_weights.h5')
pol_weights = policy_model.get_weights()
print('pol_weights')
print(pol_weights)
input('')
loading == 'n'
save_weights_flag = 1
episode = 0
while episode < num_episodes:
trajectories = run_policy(env, policy, scaler, logger, episodes=batch_size)
episode += len(trajectories)
if episode <= batch_size:
if loading == 'l':
traj = open('trajectories.obj', 'rb')
trajectories = pickle.load(traj)
traj.close()
print('342')
input('')
elif episode == num_episodes-batch_size:
traj = open('trajectories.obj','wb')
pickle.dump(trajectories,traj)
traj.close()
print('348')
input('')
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories, gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger, episode)
policy.update(observes, actions, advantages, logger) # update policy
if episode > 50:
policy_model = policy.get_trpo_policy_model()
print('about to save model')
input('')
policy_model.save('policy_model')
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
logger.close()
if save_weights_flag == 1:
valNN_model.save('val_weights.h5')
policy_weights = policy_model.get_weights()
print('policy_weights')
print(policy_weights)
input('')
# policy_model.save_weights('pol_weights.hdf5')
policy_model.save_weights('pol_weights.h5')
def main(env_name, num_episodes, gamma, lamda, kl_targ, batch_size, hid1_mult, init_pol_logvar, animate,\
save_video, num_episodes_sim, task_params, task_name):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lamda: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
init_pol_logvar: natural log of initial policy variance
save_video: Boolean determining if videos of the agent will be saved
num_episodes_sim: Number of episodes to simulate/save videos for
task_params: list of parameters to modify each environment for a different task
task_name: name user assigns to the task being used to modify the environment
"""
# **************** Environment Initialization and Paths ***************
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
# Paths
print("\n\n---- PATHS: ----")
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now) # logger object
aigym_path = os.path.join('./videos', env_name, task_name,
now) # videos folders
agent_path = os.path.join('agents', env_name,
now) # agent / policy folders
os.makedirs(agent_path)
print("Path for Saved Videos: {}".format(aigym_path))
print("Path for Saved Agents: {}\n".format(agent_path))
# Initialize Policy, Value Networks and Scaler
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, init_pol_logvar)
run_policy(env, policy, scaler, logger,
episodes=5) # run some episodes to initialize scaler
# Start Trainning
animate = True if animate == "True" else False
save_video = True if save_video == "True" else False
saver_perc = int(
num_episodes *
0.02) # determinines when the agent and video should be saved
saver_offset = saver_perc
killer = GracefulKiller()
episode = 0
while episode < num_episodes:
# Obtain 'batch_size' trajectories and add additional intermediate calculations
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size,
animate=animate)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lamda) # calculate advantage
# Concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# Logging Stats
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
# Update Policy and Value Networks
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
# Store Policy, Value Network and Scaler: every 20% of total episodes or in first/last episode
if episode >= saver_offset or episode >= num_episodes or episode <= batch_size or killer.kill_now:
# TODO: Make saving agent/video a method so that it can be called in killer.kill_now
saver_offset += saver_perc
policy.tf_saver.save(policy.sess, "{}/policy_ep_{}".format(
agent_path, episode)) # Save Policy Network
val_func.tf_saver.save(val_func.sess, "{}/val_func_ep_{}".format(
agent_path, episode)) # Save Value Network
pickle.dump(
scaler,
open("{}/scaler_ep_{}.p".format(agent_path, episode), 'wb'))
print("---- Saved Agent at Episode {} ----".format(episode))
# Save video of current agent/policy
if save_video:
print("---- Saving Video at Episode {} ----".format(episode))
_ = sim_agent(
env,
policy,
scaler,
num_episodes_sim,
save_video=True,
out_dir=aigym_path +
"/vid_ep_{}/{}_{}".format(episode, task_name, task))
env.close() # closes window open by monitor wrapper
env, _, _ = init_gym(
env_name
) # Recreate env as it is killed when saving videos
print("\n\n")
# If Ctrl + C is Pressed, ask user if Trainning shall be terminated
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
# Terminate Sessions
env.close()
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name, False)
if time_state:
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H-%M-%S") # create unique directories
logger = Logger(logname=env_name, now=now)
scaler = Scaler(obs_dim, env_name)
val_func = NNValueFunction(obs_dim, env_name, True)
arg = [obs_dim, act_dim, kl_targ, time_state, env_name]
policy = Policy(obs_dim, act_dim, kl_targ, env_name, True)
episode = 0
#progresses = None
while episode < num_episodes:
trajectories, progress = run_policy(env,
policy,
scaler,
logger,
arg,
episodes=batch_size)
#TODO change init setup
try:
progresses
except:
progresses = progress
else:
progresses = np.concatenate([progresses, progress], 1)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
scaler.save()
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
path = os.path.join('savedmodel/' + env_name)
path = os.path.join(path, 'prog.dat')
progresses.dump(path)
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar, print_results, risk_targ):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now_utc = datetime.utcnow() # create unique directories
now = str(now_utc.day) + '-' + now_utc.strftime('%b') + '-' + str(
now_utc.year) + '_' + str(
((now_utc.hour - 4) % 24)) + '.' + str(now_utc.minute) + '.' + str(
now_utc.second) # adjust for Montreal Time Zone
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
#env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, policy_logvar,
risk_targ, 'CVaR', batch_size, 1)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
kl_terms = np.array([])
beta_terms = np.array([])
if print_results:
rew_graph = np.array([])
mean_rew_graph = np.array([])
#big_li_rew_nodisc0 = np.array([])
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
#predicted_values_0 = [t['values'][0] for t in trajectories]
add_disc_sum_rew(
trajectories, gamma, scaler.mean_rew,
np.sqrt(scaler.var_rew)) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam, scaler.mean_rew,
np.sqrt(scaler.var_rew)) # calculate advantage
nodisc0 = -0.0001 * np.array(
[t['rewards'].sum() for t in trajectories]) # scaled for gradients
print(nodisc0)
disc0 = [t['disc_sum_rew'][0] for t in trajectories]
print('scaled sum rewards', nodisc0)
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
lamb = policy.update(observes, actions, advantages, nodisc0,
logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
kl_terms = np.append(kl_terms, policy.check_kl)
x1 = list(range(1, (len(kl_terms) + 1)))
rewards = plt.plot(x1, kl_terms)
plt.title('RAPPO')
plt.xlabel("Episode")
plt.ylabel("KL Divergence")
plt.savefig("KL_curve.png")
plt.close("KL_curve.png")
beta_terms = np.append(beta_terms, policy.beta)
x2 = list(range(1, (len(beta_terms) + 1)))
mean_rewards = plt.plot(x2, beta_terms)
plt.title('RAPPO')
plt.xlabel("Batch")
plt.ylabel("Beta Lagrange Multiplier")
plt.savefig("lagrange_beta_curve.png")
plt.close("lagrange_beta_curve.png")
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
if print_results:
rew_graph = np.append(rew_graph, disc0)
x1 = list(range(1, (len(rew_graph) + 1)))
rewards = plt.plot(x1, rew_graph)
plt.title('RAPPO')
plt.xlabel("Episode")
plt.ylabel("Discounted sum of rewards")
plt.savefig("learning_curve.png")
plt.close()
mean_rew_graph = np.append(mean_rew_graph, np.mean(disc0))
x2 = list(range(1, (len(mean_rew_graph) + 1)))
mean_rewards = plt.plot(x2, mean_rew_graph)
plt.title('RAPPO')
plt.xlabel("Batch")
plt.ylabel("Mean of Last Batch")
plt.savefig("learning_curve2.png")
plt.close()
if print_results:
tr = run_policy(env, policy, scaler, logger, episodes=1000)
sum_rewww = [t['rewards'].sum() for t in tr]
hist_dat = np.array(sum_rewww)
fig = plt.hist(hist_dat, bins=2000, edgecolor='b', linewidth=1.2)
plt.title('RAPPO')
plt.xlabel("Sum of Rewards")
plt.ylabel("Frequency")
plt.savefig("RA_ppo.png")
plt.close()
with open('sum_rew_final_policy.pkl', 'wb') as f:
pickle.dump(sum_rewww, f)
logger.final_log()
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar, **kwargs):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
memory = deque([])
memory_size = kwargs['memory_size']
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
target_policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult,
policy_logvar) # kl_targ = 0?
explore_policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult,
policy_logvar)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, target_policy, scaler, logger, episodes=5, fix_drct_dist=0)
run_policy(env,
explore_policy,
scaler,
logger,
episodes=5,
fix_drct_dist=0)
episode = 0
fix_drct_dist_range = (0.3, 0)
while episode < num_episodes:
# save model
if episode % 200 == 0:
save_path = target_policy.saver.save(
target_policy.sess,
"/home/csc63182/testspace/models/halfcheetah-trpo/model-%d.ckpt"
% (episode))
# run a few episodes
fix_drct_dist = (
(episode * fix_drct_dist_range[1]) +
(num_episodes - episode) * fix_drct_dist_range[0]) / num_episodes
target_trajectories = run_policy(env,
target_policy,
scaler,
logger,
episodes=batch_size,
fix_drct_dist=0)
explore_trajectories = run_policy(env,
explore_policy,
scaler,
logger,
episodes=batch_size,
fix_drct_dist=fix_drct_dist)
# Add to memory
n_explore = max(0, int(batch_size * (1 - episode / num_episodes)) - 1)
trajectories = target_trajectories + explore_trajectories[:n_explore]
episode += batch_size
memory += trajectories
while len(memory) > memory_size:
memory.popleft()
# train explore network
add_value(explore_trajectories,
val_func) # add estimated values to episodes
add_disc_sum_rew(explore_trajectories,
gamma) # calculated discounted sum of Rs
add_gae(explore_trajectories, gamma, lam) # calculate advantage
observes, actions, advantages, disc_sum_rew = build_train_set(
explore_trajectories)
explore_policy.update(observes, actions, advantages,
logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
# train target network
# re-sample trajectories
trajectories = sample(memory, batch_size)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
target_policy.update(observes, actions, advantages,
logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
with open('rewards_%s.txt' % kwargs['log_postfix'], 'w') as f:
for reward in rewards_record:
f.write('%f\n' % reward)
plt.plot((np.arange(len(rewards_record)) + 1) * batch_size, rewards_record)
plt.savefig('learning_curve_%s.png' % kwargs['log_postfix'])
logger.close()
explore_policy.close_sess()
target_policy.close_sess()
val_func.close_sess()
def main(num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar):
""" Main training loop
Args:
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym()
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, policy_logvar)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, episodes=5)
episode = 0
#Inizialize reward list (to keep track of improvements)
avg_rew_list = []
while episode < num_episodes:
print(episode)
trajectories = run_policy(env, policy, scaler, episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
policy.update(observes, actions, advantages) # update policy
val_func.fit(observes, disc_sum_rew) # update value function
avg_rew_list.append(avg_rewards(trajectories))
#Save every 20000 epidodes models (value_func, policy, scaler) and average rewards
if not episode % 20000:
print("Saving models")
policy.save(episode)
val_func.save(episode)
f = open("models/scaler-" + str(episode) + ".pkl", 'wb')
pickle.dump(scaler, f, pickle.HIGHEST_PROTOCOL)
f.close()
f2 = open("models/rewards-" + str(episode) + ".pkl", 'wb')
pickle.dump(deepcopy(avg_rew_list), f2, pickle.HIGHEST_PROTOCOL)
f2.close()
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
#Show animation at the end of training
while True:
obs = env.reset()
step = 0.0
scale, offset = scaler.get()
scale[-1] = 1.0
offset[-1] = 0.0
done = False
while not done:
obs = obs.astype(np.float32).reshape((1, -1))
obs = np.append(obs, [[step]], axis=1)
obs = (obs - offset) * scale
action = policy.sample(obs).reshape((1, -1)).astype(np.float32)
obs, reward, done, _ = env.step(np.squeeze(action, axis=0))
env.render1()
env.render2()
step += 1e-3
policy.close_sess()
val_func.close_sess()
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar, clipping_range):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
"""
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim, act_dim, kl_targ, hid1_mult, policy_logvar,
clipping_range)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
if episode % 100 == 0:
policy.save_sess()
logger.close()
policy.close_sess()
val_func.close_sess()
def main(env_name, num_iterations, gamma, lam, kl_targ, batch_size, hid1_mult,
policy_logvar, coef, use_lr_adjust, ada_kl_penalty, seed, epochs,
phi_epochs, max_timesteps, reg_scale, phi_lr, phi_hs, policy_size,
phi_obj):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_iterations: maximum number of iterations to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
hid1_mult: hid1 size for policy and value_f (mutliplier of obs dimension)
policy_logvar: natural log of initial policy variance
coef: coefficient of Stein control variate
use_lr_adjust: whether adjust lr based on kl
ada_kl_penalty: whether adjust kl penalty
max_timesteps: maximum time steps per trajectory
reg_scale: regularization coefficient
policy_size: policy network size
phi_obj: FitQ or MinVar
"""
env, obs_dim, act_dim = init_gym(env_name)
set_global_seeds(seed)
env.seed(seed)
env._max_episode_steps = max_timesteps
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
now = datetime.utcnow().strftime("%b-%d_%H:%M:%S")
aigym_path = os.path.join('log-files/', env_name, now)
env = wrappers.Monitor(env, aigym_path, force=True, video_callable=False)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, hid1_mult)
policy = Policy(obs_dim,
act_dim,
kl_targ,
hid1_mult,
policy_logvar,
epochs,
phi_epochs,
policy_size=policy_size,
phi_hidden_sizes=phi_hs,
c_ph=coef,
reg_scale=reg_scale,
lr_phi=phi_lr,
phi_obj=phi_obj)
# run a few episodes of untrained policy to initialize scaler:
run_policy(env,
policy,
scaler,
batch_size=1000,
max_timesteps=max_timesteps)
for _ in range(num_iterations):
logger.log("\n#Training Iter %d" % (_))
logger.log("Draw Samples..")
trajectories = run_policy(env,
policy,
scaler,
batch_size=batch_size,
max_timesteps=max_timesteps)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew)
logger.log("Starting Training...")
policy.update(observes, actions, advantages, \
use_lr_adjust, ada_kl_penalty) # update policy
val_func.fit(observes, disc_sum_rew) # update value function
logger.log('--------------------------------\n')
policy.close_sess()
val_func.close_sess()
class Experiment:
def __init__(self, env_name, discount, num_iterations, lamb, animate,
kl_target, show):
self.env_name = env_name
self.env = gym.make(env_name)
if env_name == "FetchReach-v0":
self.env = gym.wrappers.FlattenDictWrapper(
self.env, ['observation', 'desired_goal', 'achieved_goal'])
gym.spaces.seed(1234)
self.obs_dim = self.env.observation_space.shape[
0] + 1 # adding time step as feature
self.act_dim = self.env.action_space.shape[0]
self.discount = discount
self.num_iterations = num_iterations
self.lamb = lamb
self.animate = animate
self.buffer = Buffer(50000, self.obs_dim, self.act_dim)
self.episodes = 20
self.killer = GracefulKiller()
self.policy = QPropPolicy(self.obs_dim,
self.act_dim,
self.env.action_space,
kl_target,
epochs=20)
self.critic = DeterministicCritic(self.obs_dim, self.act_dim,
self.discount, OUTPATH)
# using MC return would be more helpful
self.value_func = l2TargetValueFunc(self.obs_dim, epochs=10)
if not show:
# save copies of file
shutil.copy(inspect.getfile(self.policy.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.value_func.__class__), OUTPATH)
shutil.copy(inspect.getfile(self.__class__), OUTPATH)
self.log_file = open(OUTPATH + 'log.csv', 'w')
self.write_header = True
print('observation dimension:', self.obs_dim)
print('action dimension:', self.act_dim)
# Use of a scaler is crucial
self.scaler = Scaler(self.obs_dim)
self.init_scaler()
def init_scaler(self):
"""
5 episodes empirically determined.
:return:
"""
print('Fitting scaler')
observation_samples = []
for i in range(5):
observation = []
obs = self.env.reset()
observation.append(obs)
obs = obs.astype(np.float64).reshape((1, -1))
done = False
step = 0
while not done:
obs = np.append(obs, [[step]], axis=1) # add time step feature
action = self.policy.get_sample(obs).reshape(
(1, -1)).astype(np.float64)
if self.env_name == "FetchReach-v0":
obs_new, reward, done, _ = self.env.step(
action.reshape(-1))
else:
obs_new, reward, done, _ = self.env.step(action)
observation.append(obs_new)
obs = obs_new.astype(np.float64).reshape((1, -1))
step += 1e-3
observation_samples.append(observation)
observation_samples = np.concatenate(observation_samples, axis=0)
self.scaler.update(observation_samples)
def normalize_obs(self, obs):
"""
transform and update on the fly.
:param obs:
:return:
"""
scale, offset = self.scaler.get()
obs_scaled = (obs - offset) * scale
self.scaler.update(obs.astype(np.float64).reshape((1, -1)))
return obs_scaled
def run_one_episode(self):
"""
collect data only
:param save:
:param train_policy:
:param train_value_func:
:param animate:
:return:
"""
obs = self.env.reset()
observes, actions, rewards = [], [], []
done = False
step = 0
while not done:
if self.animate:
self.env.render()
obs = obs.astype(np.float64).reshape((1, -1))
obs = self.normalize_obs(obs)
obs = np.append(obs, [[step]], axis=1) # add time step feature
observes.append(obs)
action = self.policy.get_sample(obs).reshape(
(1, -1)).astype(np.float64)
actions.append(action)
if self.env_name == "FetchReach-v0":
obs_new, reward, done, _ = self.env.step(action.reshape(-1))
else:
obs_new, reward, done, _ = self.env.step(action)
if not isinstance(reward, float):
reward = np.asscalar(reward)
rewards.append(reward)
obs = obs_new
step += 0.003
return np.concatenate(observes), np.concatenate(actions), np.array(
rewards)
def discounted_sum(self, l, factor):
discounted = []
sum = 0
for i in reversed(l):
discounted.append(factor * sum + i)
sum = factor * sum + i
return np.array(list(reversed(discounted)))
def run_policy(self, episodes):
"""
gather a batch of samples.
:param episodes:
:return:
"""
trajectories = []
for e in range(episodes):
observes, actions, rewards = self.run_one_episode()
trajectory = {
'observes': observes,
'actions': actions,
'rewards': rewards,
'scaled_rewards': rewards * (1 - self.discount)
}
trajectories.append(trajectory)
return trajectories
def run_expr(self):
ep_steps = []
ep_rewards = []
ep_entropy = []
i = 0
while i < self.num_iterations:
trajectories = self.run_policy(20)
# add to experience replay buffer
self.buffer.append(trajectories)
print('buffer size:', self.buffer.size())
i += len(trajectories)
# for E=20, T=50, the total number of samples would be 1000
# In future needs to account for not uniform time steps per episode.
# e.g. in Hopper-v2 environment not every episode has same time steps
# E = len(trajectories)
# num_samples = np.sum([len(t['rewards']) for t in trajectories])
gradient_steps = np.sum([len(t['rewards']) for t in trajectories])
if self.env_name == "FetchReach-v0":
assert (gradient_steps == 20 * 50)
"""train critic"""
# train all samples in the buffer, to the extreme
# self.critic.fit(self.policy, self.buffer, epochs=10, num_samples=self.buffer.size())
# train some samples minibatches only
self.critic.another_fit_func(self.policy, self.buffer, 5000)
"""calculation of episodic discounted return only needs rewards"""
mc_returns = np.concatenate([
self.discounted_sum(t['scaled_rewards'], self.discount)
for t in trajectories
])
"""using current batch of samples to update baseline"""
observes = np.concatenate([t['observes'] for t in trajectories])
actions = np.concatenate([t['actions'] for t in trajectories])
value_func_loss = self.value_func.update(observes, mc_returns)
"""compute GAE"""
for t in trajectories:
t['values'] = self.value_func.predict(t['observes'])
# IS it really legitimate to insert 0 at the last obs?
t['td_residual'] = t[
'scaled_rewards'] + self.discount * np.append(
t['values'][1:], 0) - t['values']
t['gae'] = self.discounted_sum(t['td_residual'],
self.discount * self.lamb)
advantages = np.concatenate([t['gae'] for t in trajectories])
advantages = (advantages - advantages.mean()) / (advantages.std() +
1e-6)
"""compute control variate""" ""
cv = self.critic.get_contorl_variate(self.policy, observes,
actions)
"""conservative control variate"""
eta = [1 if i > 0 else 0 for i in advantages * cv]
"""center learning signal"""
# check that advantages and CV should be of size E*T
# eta controls the on-off of control variate
learning_signal = advantages - eta * cv
# learning_signal = (learning_signal - learning_signal.mean()) / (learning_signal.std() + 1e-6)
"""controlled taylor eval term"""
ctrl_taylor = np.concatenate(
[[eta[i] * act] for i, act in enumerate(
self.critic.get_taylor_eval(self.policy, observes))])
policy_loss, kl, entropy, beta = self.policy.update(
observes, actions, learning_signal, ctrl_taylor)
# normalize advantage estimates
# advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-6)
avg_rewards = np.sum(
np.concatenate([t['rewards']
for t in trajectories])) / self.episodes
avg_timesteps = np.average(
[len(t['rewards']) for t in trajectories])
log = {}
# compute statistics such as mean and std
log['steps'] = avg_timesteps
log['rewards'] = avg_rewards
log['policy_loss'] = policy_loss
log['kl'] = kl
log['entropy'] = entropy
log['value_func_loss'] = value_func_loss
log['beta'] = beta
# display
print('episode: ', i)
print('average steps: {0}, average rewards: {1}'.format(
log['steps'], log['rewards']))
for key in [
'policy_loss', 'kl', 'entropy', 'beta', 'value_func_loss'
]:
print('{:s}: {:.2g}'.format(key, log[key]))
print('\n')
ep_steps.append(log['steps'])
ep_rewards.append(log['rewards'])
ep_entropy.append(log['entropy'])
# write to log.csv
if self.write_header:
fieldnames = [x for x in log.keys()]
self.writer = csv.DictWriter(self.log_file,
fieldnames=fieldnames)
self.writer.writeheader()
self.write_header = False
self.writer.writerow(log)
# we want the csv file to preserve information even if the program terminates earlier than scheduled.
self.log_file.flush()
# save model weights if stopped manually
if self.killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
self.killer.kill_now = False
# if (i+1)%20 == 0:
# print('episode: ', i+1)
# print('average steps', np.average(steps))
# print('average rewards', np.average(rewards))
self.policy.save(OUTPATH)
self.value_func.save(OUTPATH)
self.scaler.save(OUTPATH)
plt.figure(figsize=(12, 9))
if self.env_name.startswith('Fetch'):
ax1 = plt.subplot(121)
plt.xlabel('episodes')
plt.ylabel('policy entropy')
plt.plot(ep_entropy)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(
lambda x, pos: '{0:g}'.format(x * scale_x))
ax1.xaxis.set_major_formatter(ticks_x)
else:
ax1 = plt.subplot(121)
plt.xlabel('episodes')
plt.ylabel('steps')
plt.plot(ep_steps)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(
lambda x, pos: '{0:g}'.format(x * scale_x))
ax1.xaxis.set_major_formatter(ticks_x)
ax2 = plt.subplot(122)
plt.xlabel('episodes')
plt.ylabel('episodic rewards')
plt.plot(ep_rewards)
scale_x = self.episodes
ticks_x = ticker.FuncFormatter(
lambda x, pos: '{0:g}'.format(x * scale_x))
ax2.xaxis.set_major_formatter(ticks_x)
plt.savefig(OUTPATH + 'train.png')
def load_model(self, load_from):
from tensorflow.python.tools import inspect_checkpoint as chkp
# # print all tensors in checkpoint file
# chkp.print_tensors_in_checkpoint_file(load_from+'policy/policy.pl', tensor_name='', all_tensors=True, all_tensor_names=True)
self.policy.load(load_from + 'policy/policy.pl')
self.value_func.load(load_from + 'value_func/value_func.pl')
def demonstrate_agent(self, load_from):
self.load_model(load_from)
with open(load_from + "scaler.pkl", 'rb') as file:
self.scaler = pickle.load(file)
self.animate = True
for i in range(10):
observes, actons, rewards = self.run_one_episode()
ep_rewards = np.sum(rewards)
ep_steps = len(rewards)
print("Total steps: {0}, total rewards: {1}\n".format(
ep_steps, ep_rewards))
class Agent:
#Warning! policy.py and critic.py are still work in progress and contain many global variables that should be converted to
#class member variables. Before that is done, all instances of Agent must use the same values for the following:
#PPOepsilon,nHidden,nUnitsPerLayer,activation,H,entropyLossWeight,sdLowLimit
def __init__(self,
stateDim: int,
actionDim: int,
actionMin: np.array,
actionMax: np.array,
learningRate=0.0005,
gamma=0.99,
GAElambda=0.95,
PPOepsilon=0.2,
PPOentropyLossWeight=0,
nHidden: int = 2,
nUnitsPerLayer: int = 128,
mode="PPO-CMA-m",
activation="lrelu",
H: int = 9,
entropyLossWeight: float = 0,
sdLowLimit=0.01,
useScaler: bool = True,
criticTimestepScale=0.001):
#Create policy network
print("Creating policy")
self.actionMin = actionMin.copy()
self.actionMax = actionMax.copy()
self.actionDim = actionDim
self.stateDim = stateDim
self.useScaler = useScaler
if useScaler:
self.scaler = Scaler(stateDim)
self.scalerInitialized = False
self.normalizeAdvantages = True
self.gamma = gamma
self.GAElambda = GAElambda
self.criticTimestepScale = 0 if gamma == 0 else criticTimestepScale #with gamma==0, no need for this
piEpsilon = None
nHistory = 1
negativeAdvantageAvoidanceSigma = 0
if mode == "PPO-CMA" or mode == "PPO-CMA-m":
usePPOLoss = False #if True, we use PPO's clipped surrogate loss function instead of the standard -A_i * log(pi(a_i | s_i))
separateVarAdapt = True
self.reluAdvantages = True if mode == "PPO-CMA" else False
nHistory = H #policy mean adapts immediately, policy covariance as an aggreagate of this many past iterations
useSigmaSoftClip = True
negativeAdvantageAvoidanceSigma = 1 if mode == "PPO-CMA-m" else 0
elif mode == "PPO":
usePPOLoss = True #if True, we use PPO's clipped surrogate loss function instead of the standard -A_i * log(pi(a_i | s_i))
separateVarAdapt = False
# separateSigmaAdapt=False
self.reluAdvantages = False
useSigmaSoftClip = True
piEpsilon = 0
else:
raise ("Unknown mode {}".format(mode))
self.policy = Policy(
stateDim,
actionDim,
actionMin,
actionMax,
entropyLossWeight=PPOentropyLossWeight,
networkActivation=activation,
networkDepth=nHidden,
networkUnits=nUnitsPerLayer,
networkSkips=False,
learningRate=learningRate,
minSigma=sdLowLimit,
PPOepsilon=PPOepsilon,
usePPOLoss=usePPOLoss,
separateVarAdapt=separateVarAdapt,
nHistory=nHistory,
useSigmaSoftClip=useSigmaSoftClip,
piEpsilon=piEpsilon,
negativeAdvantageAvoidanceSigma=negativeAdvantageAvoidanceSigma)
#Create critic network, +1 stateDim because at least in OpenAI gym, episodes are time-limited and the value estimates thus depend on simulation time.
#Thus, we use time step as an additional feature for the critic.
#Note that this does not mess up generalization, as the feature is not used for the policy during training or at runtime
print("Creating critic network")
self.critic = Critic(stateDim=stateDim + 1,
learningRate=learningRate,
nHidden=nHidden,
networkUnits=nUnitsPerLayer,
networkActivation=activation,
useSkips=False,
lossType="L1")
#Experience trajectory buffers for the memorize() and updateWithMemorized() methods
self.experienceTrajectories = []
self.currentTrajectory = []
#call this after tensorflow's global variables initializer
def init(self, sess: tf.Session, verbose=False):
#Pretrain the policy to output the initial Gaussian for all states
self.policy.init(
sess, 0, 1,
0.5 * (self.actionMin + self.actionMax) * np.ones(self.actionDim),
0.5 * (self.actionMax - self.actionMin) * np.ones(self.actionDim),
256, 2000, verbose)
#stateObs is an n-by-m tensor, where n = number of observations, m = number of observation variables
def act(self,
sess: tf.Session,
stateObs: np.array,
deterministic=False,
clipActionToLimits=True):
#Expand a single 1d-observation into a batch of 1 vectors
if len(stateObs.shape) == 1:
stateObs = np.reshape(stateObs, [1, stateObs.shape[0]])
#Query the policy for the action, except for the first iteration where we sample directly from the initial exploration Gaussian
#that covers the whole action space.
#This is done because we don't know the scale of state observations a priori; thus, we can only init the state scaler in update(),
#after we have collected some experience.
if self.useScaler and (not self.scalerInitialized):
actions = np.random.normal(
0.5 * (self.actionMin + self.actionMax) *
np.ones(self.actionDim),
0.5 * (self.actionMax - self.actionMin) *
np.ones(self.actionDim),
size=[stateObs.shape[0], self.actionDim])
if clipActionToLimits:
actions = np.clip(
actions, np.reshape(self.actionMin, [1, self.actionDim]),
np.reshape(self.actionMax, [1, self.actionDim]))
return actions
else:
if self.useScaler:
scaledObs = self.scaler.process(stateObs)
else:
scaledObs = stateObs
if deterministic:
actions = self.policy.getExpectation(sess, scaledObs)
else:
actions = self.policy.sample(sess, scaledObs)
if clipActionToLimits:
actions = np.clip(actions, self.actionMin, self.actionMax)
return actions
def memorize(self, observation: np.array, action: np.array, reward: float,
nextObservation: np.array, done: bool):
e = Experience(observation, action, reward, nextObservation, done)
self.currentTrajectory.append(e)
if done:
self.experienceTrajectories.append(self.currentTrajectory)
self.currentTrajectory = []
def getAverageActionStdev(self):
if self.useScaler and (not self.scalerInitialized):
return np.mean(0.5 * (self.actionMax - self.actionMin))
else:
return self.policy.usedSigmaSum / (1e-20 +
self.policy.usedSigmaSumCounter)
#If you call memorize() after each action, you can update the agent with this method.
#If you handle the experience buffers yourself, e.g., due to a multithreaded implementation, use the update() method instead.
def updateWithMemorized(self,
sess: tf.Session,
batchSize: int = 512,
nBatches: int = 100,
verbose=True,
valuesValid=False,
timestepsValid=False):
self.update(sess,
experienceTrajectories=self.experienceTrajectories,
batchSize=batchSize,
nBatches=nBatches,
verbose=verbose,
valuesValid=valuesValid,
timestepsValid=timestepsValid)
averageEpisodeReturn = 0
for t in self.experienceTrajectories:
episodeReturn = 0
for e in t:
episodeReturn += e.r
averageEpisodeReturn += episodeReturn
averageEpisodeReturn /= len(self.experienceTrajectories)
self.experienceTrajectories = []
self.currentTrajectory = []
return averageEpisodeReturn
#experienceTrajectories is a list of lists of Experience instances such that each of the contained lists corresponds to an episode simulation trajectory
def update(self,
sess: tf.Session,
experienceTrajectories,
batchSize: int = 512,
nBatches: int = 100,
verbose=True,
valuesValid=False,
timestepsValid=False):
trajectories = experienceTrajectories #shorthand
#Collect all data into linear arrays for training.
nTrajectories = len(trajectories)
nData = 0
for trajectory in trajectories:
nData += len(trajectory)
#propagate values backwards along trajectory if not already done
if not valuesValid:
for i in reversed(range(len(trajectory) - 1)):
#value estimates, used for training the critic and estimating advantages
trajectory[i].V = trajectory[
i].r + self.gamma * trajectory[i + 1].V
#update time steps if not updated
if not timestepsValid:
for i in range(len(trajectory)):
trajectory[i].timeStep = i
allStates = np.zeros([nData, self.stateDim])
allActions = np.zeros([nData, self.actionDim])
allValues = np.zeros([nData])
allTimes = np.zeros([nData, 1])
k = 0
for trajectory in trajectories:
for e in trajectory:
allStates[k, :] = e.s
allValues[k] = e.V
allActions[k, :] = e.a
allTimes[k, 0] = e.timeStep * self.criticTimestepScale
k += 1
#Update scalers
if self.useScaler:
self.scaler.update(allStates)
scale, offset = self.scaler.get()
self.scalerInitialized = True
else:
offset = 0
scale = 1
#Scale the observations for training the critic
scaledStates = self.scaler.process(allStates)
#Train critic
def augmentCriticObs(obs: np.array, timeSteps: np.array):
return np.concatenate([obs, timeSteps], axis=1)
self.critic.train(sess,
augmentCriticObs(scaledStates, allTimes),
allValues,
batchSize,
nEpochs=0,
nBatches=nBatches,
verbose=verbose)
#Policy training needs advantages, which depend on the critic we just trained.
#We use Generalized Advantage Estimation by Schulman et al.
if verbose:
print("Estimating advantages...".format(len(trajectories)))
for t in trajectories:
#query the critic values of all states of this trajectory in one big batch
nSteps = len(t)
states = np.zeros([nSteps + 1, self.stateDim])
timeSteps = np.zeros([nSteps + 1, 1])
for i in range(nSteps):
states[i, :] = t[i].s
timeSteps[i, 0] = t[i].timeStep * self.criticTimestepScale
states[nSteps, :] = t[nSteps - 1].s_next
states = (states - offset) * scale
values = self.critic.predict(sess,
augmentCriticObs(states, timeSteps))
#GAE loop, i.e., take the instantaneous advantage (how much value a single action brings, assuming that the
#values given by the critic are unbiased), and smooth those along the trajectory using 1st-order IIR filter.
for step in reversed(range(nSteps - 1)):
delta_t = t[step].r + self.gamma * values[step +
1] - values[step]
t[step].advantage = delta_t + self.GAElambda * self.gamma * t[
step + 1].advantage
#Gather the advantages to linear array and apply ReLU and normalization if needed
allAdvantages = np.zeros([nData])
k = 0
for trajectory in trajectories:
for e in trajectory:
allAdvantages[k] = e.advantage
k += 1
if self.reluAdvantages:
allAdvantages = np.clip(allAdvantages, 0, np.inf)
if self.normalizeAdvantages:
aMean = np.mean(allAdvantages)
aSd = np.std(allAdvantages)
if verbose:
print("Advantage mean {}, sd{}".format(aMean, aSd))
allAdvantages /= 1e-10 + aSd
#Train policy. Note that this uses original unscaled states, because the PPO-CMA variance training needs a history of
#states in the same scale
self.policy.train(sess,
allStates,
allActions,
allAdvantages,
batchSize,
nEpochs=0,
nBatches=nBatches,
stateOffset=offset,
stateScale=scale,
verbose=verbose)
def main(env_name, num_episodes, gamma, lamda, kl_targ, clipping_range, pol_loss_type, batch_size, init_pol_logvar, animate,\
save_video, save_rate, num_episodes_sim, task_params, task_name, dims_core_hid, dims_head_hid, act_func_name,\
episode_to_load, now_to_load):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lamda: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
clipping_range: max value to clip the policy gradient ratio
pol_loss_type: string determining which type of loss to use for the Policy Network
batch_size: number of episodes per policy training batch
init_pol_logvar: natural log of initial policy variance
save_video: Boolean determining if videos of the agent will be saved
save_rate: Int determining how often to save videos for
num_episodes_sim: Number of episodes to simulate/save videos for
task_params: list of parameters to modify each environment for a different task
task_name: name user assigns to the task being used to modify the environment
"""
# **************** Environment Initialization and Paths ***************
task_params_str = ''.join(str(e) +', ' for e in task_params)
num_tasks = len(task_params)
envs = [None]*num_tasks
scalers = [None]*num_tasks
loggers = [None]*num_tasks
print ("\n\n------ PATHS: ------")
start_time = datetime.now()
if episode_to_load == None: now = start_time.strftime("%b-%d_%H:%M:%S") # If NOT loading from Checkpoint -> used to create unique directories
else:
assert now_to_load != None,\
"\n\nWARNING: Date time to load ({}) was not provided. Please provide a valid date time of an experiment".format(now_to_load)
now = now_to_load
logs_path = os.path.join('log-files', env_name, task_name, task_params_str, now)
for task in range(num_tasks):
# Create task specific environment
envs[task], obs_dim, act_dim = init_gym(env_name, task_param = task_params[task])
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
# Create task specific Paths and logger object
loggers[task] = Logger(logname= [env_name, task_name, task_params_str], now=now, \
logname_file= "_{}_{}".format(task_name, task_params[task]))
if episode_to_load == None: # If NOT loading from Checkpoint
scalers[task] = Scaler(obs_dim)
# Auxiliary saver (becase logger sometimes fails or takes to much time)
with open(logs_path + '/aux_{}_{}.txt'.format(task_name, task_params[task]), 'w') as f:
f.write("_Episode" + " " + "_MeanReward")
aigym_path= os.path.join('./videos', env_name, task_name, task_params_str, now) # videos folders
agent_path = os.path.join('agents', env_name , task_name, task_params_str, now) # agent / policy folders
if episode_to_load == None: # If NOT loading from Checkpoint
os.makedirs(agent_path)
with open(agent_path + '/commandline_args.txt', 'w') as f: f.write(' '.join(sys.argv[1:])) # save commandline command
with open(logs_path + '/commandline_args.txt', 'w') as f: f.write(' '.join(sys.argv[1:])) # save commandline command
print("\nPath for Saved Videos : {}".format(aigym_path))
print("Path for Saved Agents: {}\n".format(agent_path))
# **************** Initialize Policy, Value Networks and Scaler ***************
print ("\n\n------ NEURAL NETWORKS: ------")
dims_core_hid.insert(0, obs_dim) # Modify dims list to have the size of the layer 'n-1' at position '0'
dims_head_hid.insert(0, dims_head_hid[-1])
val_func = NNValueFunction(obs_dim, dims_core_hid, dims_head_hid, num_tasks)#, act_func_name)
policy = Policy(obs_dim, act_dim, dims_core_hid, dims_head_hid, num_tasks, pol_loss_type = pol_loss_type)
# Load from Checkpoint:
# Validate intented episode to load OR get last episode number if no target load episode was provided
if episode_to_load != None:
load_agent_path = agent_path # agent / policy folders
saved_ep_list = [file.split(".")[0].split("_")[-1] for file in os.listdir(load_agent_path) if "policy" in file]
if episode_to_load == -1: # Get last saved episode
episode_to_load = sorted([int(ep_string) for ep_string in saved_ep_list])[-1]
else: # Validate if episode_to_load was indeed saved
assert str(episode_to_load) in saved_ep_list,\
"\n\nWARNING: Episode you want to load ({}) was not stored during trainning".format(episode_to_load)
# Load Policy Network's Ops and Variables & Load Scaler Object
policy.tf_saver.restore(policy.sess, "{}/policy_ep_{}".format(load_agent_path, episode_to_load))
val_func.tf_saver.restore(val_func.sess, "{}/val_func_ep_{}".format(load_agent_path, episode_to_load))
scalers = pickle.load(open("{}/scalers_ep_{}.p".format(load_agent_path, episode_to_load), 'rb'))
print("\n\n ---- CHECKPOINT LOAD: Episoded Loaded **{}**".format(episode_to_load))
# Delete extra epochs that where logged to the auxiliary logs
for task in range(num_tasks):
aux_log_path = logs_path + '/aux_{}_{}.txt'.format(task_name, task_params[task])
aux_log = pd.read_table(aux_log_path, delim_whitespace=True)
idx_to_cut = aux_log.index[aux_log["_Episode"] == episode_to_load ].tolist()[0]
aux_log[0:idx_to_cut+1].to_csv(aux_log_path, header=True, index=False, sep=' ', mode='w') # overwrite trimmed aux_log
# If NOT loading from Checkpoint: run some episodes to initialize scalers and create Tensor board dirs
elif episode_to_load == None:
for task in range(num_tasks): run_policy(envs[task], policy, scalers[task], loggers[task], episodes=5, task=task)
# Tensor Board writer
os.makedirs(agent_path + '/tensor_board/policy')
os.makedirs(agent_path + '/tensor_board/valFunc')
tb_pol_writer = tf.summary.FileWriter(agent_path + '/tensor_board/policy', graph=policy.g)
tb_val_writer = tf.summary.FileWriter(agent_path + '/tensor_board/valFunc', graph=val_func.g)
# **************** Start Training ***************
print ("\n\n------ TRAINNING: ------")
animate = True if animate == "True" else False
save_video = True if save_video == "True" else False
saver_offset = save_rate
killer = GracefulKiller()
if episode_to_load == None: episode = 0
else: episode = episode_to_load
# Episode is counted across all tasks i.e. N episodes indicates each tasks has been runned for N times
while episode < num_episodes and not killer.kill_now:
# **************** Obtain data (train set) ***************
observes_all = [None]*num_tasks
actions_all = [None]*num_tasks
advantages_all = [None]*num_tasks
disc_sum_rew_all = [None]*num_tasks
episode += batch_size
for task in range(num_tasks):
# Obtain 'batch_size' trajectories and add additional intermediate calculations
trajectories = run_policy(envs[task],policy, scalers[task], loggers[task],episodes=batch_size,task=task,animate=animate)
add_value(trajectories, val_func, task) # add estimated values to episodes
add_disc_sum_rew(trajectories, gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lamda) # calculate advantage
# Concatenate all episodes into single NumPy arrays
observes_all[task], actions_all[task], advantages_all[task], disc_sum_rew_all[task] = build_train_set(trajectories)
# Logging Stats
log_batch_stats(observes_all[task], actions_all[task], advantages_all[task], disc_sum_rew_all[task], \
loggers[task], episode)
# **************** Update Policy and Value Networks ***************
print ("*************************************")
for task in range(num_tasks):
pol_summary = policy.update(task, observes_all[task], actions_all[task], advantages_all[task], loggers[task]) # update policy
val_summary = val_func.fit(task, observes_all[task], disc_sum_rew_all[task], loggers[task]) # update value function
# Auxiliary saver (because logger sometimes fails or takes to much time)
with open(logs_path + '/aux_{}_{}.txt'.format(task_name, task_params[task]), 'a') as f:
f.write("\n" + str(loggers[task].log_entry['_Episode']) + " " + str(loggers[task].log_entry['_MeanReward']))
loggers[task].write(display=True) # write logger results to file and stdout
tb_pol_writer.add_summary(pol_summary, global_step=episode)
tb_val_writer.add_summary(val_summary, global_step=episode)
# **************** Storing NN and Videos ***************
# Store Policy, Value Network and Scaler: every 'save_rate' of total episodes or in first/last episode
if episode >= saver_offset or episode >=num_episodes or episode <=batch_size or killer.kill_now:
# TODO: Make saving agent/video a method so that it can be called in killer.kill_now
saver_offset += save_rate
policy.tf_saver.save(policy.sess, "{}/policy_ep_{}".format(agent_path, episode)) # Save Policy Network
val_func.tf_saver.save(val_func.sess, "{}/val_func_ep_{}".format(agent_path, episode)) # Save Value Network
pickle.dump(scalers, open("{}/scalers_ep_{}.p".format(agent_path, episode), 'wb'))
print ("---- Saved Agent at Episode {} ----".format(episode))
# Save video of current agent/policy
if save_video:
print ("---- Saving Video at Episode {} ----".format(episode))
for task in range(num_tasks):
print("Environment Wind: {}".format(envs[task].env.world.gravity))
_ = sim_agent(envs[task], policy, task, scalers[task], num_episodes_sim, save_video=True,
out_dir=aigym_path + "/vid_ep_{}/{}_{}".format(episode, task_name, task_params[task]))
envs[task].close() # closes window open by monitor wrapper
envs[task], _, _ = init_gym(env_name,task_param=task_params[task]) # Recreate env as it was killed
print("\n\n")
# If Ctrl + C is Pressed, ask user if Trainning shall be terminated
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
# **************** Terminate Variables **************
for task in range(num_tasks):
envs[task].close()
loggers[task].close()
policy.close_sess()
val_func.close_sess()
# Save elapsed time
end_time = datetime.now()
elapsed_time = end_time - start_time
timedelta(0, 8, 562000)
delta_time = divmod(elapsed_time.days * 86400 + elapsed_time.seconds, 60)
delta_str = "Elapsed Time: {} min {} seconds".format(delta_time[0], delta_time[1])
# save elapsed time, 'a' to append not overwrite
with open(agent_path + '/commandline_args.txt', 'a') as f: f.write('\n\n' + delta_str)
with open(logs_path + '/commandline_args.txt', 'a') as f: f.write('\n\n' + delta_str)
class Recognizer(object):
def __init__(self):
self.resize_shape = (100, 100)
shape_predictor_path = 'data/shape_predictor_68_face_landmarks.dat'
self.shape_predictor = dlib.shape_predictor(shape_predictor_path)
self.eye_and_mouth_indices = [39, 42, 57]
self.template_landmarks = get_template_landmarks(
self.eye_and_mouth_indices, self.resize_shape[0])
npload = np.load('data/mean_std2.npz')
mean, std = npload['mean'], npload['std']
self.scaler = Scaler(mean=mean, std=std)
# model_emb_path = 'data/epoch_17_test_eer0.191621.hdf5'
model_path = 'data/cnn_model/epoch_66_val_loss1.206078.hdf5'
model_emb_path = 'data/emb_model/model_10_epoch_10_test_eer0.169731_test2_err0.204908.hdf5'
# model_path = 'data/cnn_model/epoch_16_val_loss1.231896.hdf5'
# model_emb_path = 'data/emb_model/model_8_epoch_15_test_eer0.127431_test2_err0.218662.hdf5'
# model_emb_path = 'data/emb_model/model_8_epoch_1_test_eer0.133520_test2_err0.216839.hdf5'
# model_emb_path = 'data/emb_model/model_9_epoch_5_test_eer0.127574_test2_err0.229637.hdf5'
# model_path = 'data/cnn_model/epoch_232_val_loss1.351451.hdf5'
# model_emb_path = 'data/emb_model/model_1_epoch_0_test_eer0.114874.hdf5'
#
# model_path = 'data/cnn_model/epoch_57_val_loss1.699622.hdf5'
# model_emb_path = 'data/emb_model/model_2_epoch_25_test_eer0.106689.hdf5'
# model_path = 'data/cnn_model/epoch_29_val_loss1.441430.hdf5'
# model_emb_path = 'data/emb_model/model_5_epoch_2_test_eer0.143211.hdf5'
# model_emb_path = 'data/emb_model/model_6_epoch_6_test_eer_0.135497_test2_err0.254601.hdf5'
# model_emb_path = '../data/Modeltpe2/epoch_0_test_eer0.139840.hdf5'
# model_emb_path = '../data/Modeltpe3/epoch_12_test_eer0.107399.hdf5'
# model_emb_path = 'data/emb_model/model_4_epoch_1_test_eer0.108006.hdf5'
model = keras.models.load_model(model_path)
self.model_emb = keras.models.load_model(model_emb_path)
self.bottleneck = Bottleneck(model)
npload = np.load('data/face_base.npz')
self.x, self.y = npload['x'], npload['y']
print(self.x.shape, self.y.shape)
with open('data/labels_dict.pkl', 'rb') as file:
self.labels_dict = pickle.load(file)
self.knn = KNeighborsClassifier(n_neighbors=1, metric=metric, n_jobs=1)
self.knn.fit(self.x, self.y)
def iterate_similarities(self, emb):
for i, person_emb in enumerate(self.x):
sim = person_emb @ emb.T
yield sim, i
def predict(self, img, img_gray, rect):
img = align_img(img, img_gray, rect, self.shape_predictor,
self.template_landmarks, self.eye_and_mouth_indices,
self.resize_shape)
batch_x = [img]
import matplotlib.pyplot as plt
# plt.imshow(img)
# plt.show()
batch_x = self.scaler.transform(batch_x)
batch_x = self.bottleneck.predict(transpose_matrix(batch_x))
batch_x = self.model_emb.predict(batch_x)
# batch_x = self.model_emb.predict(transpose_matrix(batch_x))
pred_labels = self.knn.predict(batch_x)
neighbors = self.knn.kneighbors(batch_x)
label_neighbors = [
self.labels_dict[self.y[ind]] for ind in neighbors[1][0]
]
print(label_neighbors, neighbors[0])
# label_ind = max(self.iterate_similarities(batch_x[0]), key=lambda x: x[0])[1]
# label = self.y[label_ind]
label = pred_labels[0]
return self.labels_dict[label], label_neighbors
def __init__(self,
stateDim: int,
actionDim: int,
actionMin: np.array,
actionMax: np.array,
learningRate=0.0005,
gamma=0.99,
GAElambda=0.95,
PPOepsilon=0.2,
PPOentropyLossWeight=0,
nHidden: int = 2,
nUnitsPerLayer: int = 128,
mode="PPO-CMA-m",
activation="lrelu",
H: int = 9,
entropyLossWeight: float = 0,
sdLowLimit=0.01,
useScaler: bool = True,
criticTimestepScale=0.001):
#Create policy network
print("Creating policy")
self.actionMin = actionMin.copy()
self.actionMax = actionMax.copy()
self.actionDim = actionDim
self.stateDim = stateDim
self.useScaler = useScaler
if useScaler:
self.scaler = Scaler(stateDim)
self.scalerInitialized = False
self.normalizeAdvantages = True
self.gamma = gamma
self.GAElambda = GAElambda
self.criticTimestepScale = 0 if gamma == 0 else criticTimestepScale #with gamma==0, no need for this
piEpsilon = None
nHistory = 1
negativeAdvantageAvoidanceSigma = 0
if mode == "PPO-CMA" or mode == "PPO-CMA-m":
usePPOLoss = False #if True, we use PPO's clipped surrogate loss function instead of the standard -A_i * log(pi(a_i | s_i))
separateVarAdapt = True
self.reluAdvantages = True if mode == "PPO-CMA" else False
nHistory = H #policy mean adapts immediately, policy covariance as an aggreagate of this many past iterations
useSigmaSoftClip = True
negativeAdvantageAvoidanceSigma = 1 if mode == "PPO-CMA-m" else 0
elif mode == "PPO":
usePPOLoss = True #if True, we use PPO's clipped surrogate loss function instead of the standard -A_i * log(pi(a_i | s_i))
separateVarAdapt = False
# separateSigmaAdapt=False
self.reluAdvantages = False
useSigmaSoftClip = True
piEpsilon = 0
else:
raise ("Unknown mode {}".format(mode))
self.policy = Policy(
stateDim,
actionDim,
actionMin,
actionMax,
entropyLossWeight=PPOentropyLossWeight,
networkActivation=activation,
networkDepth=nHidden,
networkUnits=nUnitsPerLayer,
networkSkips=False,
learningRate=learningRate,
minSigma=sdLowLimit,
PPOepsilon=PPOepsilon,
usePPOLoss=usePPOLoss,
separateVarAdapt=separateVarAdapt,
nHistory=nHistory,
useSigmaSoftClip=useSigmaSoftClip,
piEpsilon=piEpsilon,
negativeAdvantageAvoidanceSigma=negativeAdvantageAvoidanceSigma)
#Create critic network, +1 stateDim because at least in OpenAI gym, episodes are time-limited and the value estimates thus depend on simulation time.
#Thus, we use time step as an additional feature for the critic.
#Note that this does not mess up generalization, as the feature is not used for the policy during training or at runtime
print("Creating critic network")
self.critic = Critic(stateDim=stateDim + 1,
learningRate=learningRate,
nHidden=nHidden,
networkUnits=nUnitsPerLayer,
networkActivation=activation,
useSkips=False,
lossType="L1")
#Experience trajectory buffers for the memorize() and updateWithMemorized() methods
self.experienceTrajectories = []
self.currentTrajectory = []
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, eval):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
"""
if eval:
print("Evaluating: ")
evaluate(env_name, num_episodes)
exit()
killer = GracefulKiller()
env, obs_dim, act_dim = init_gym(env_name)
now = datetime.utcnow().strftime(
"%b-%d_%H:%M:%S") # create unique directories
logger = Logger(logname=env_name, now=now)
aigym_path = os.path.join('/tmp', env_name, now)
#env = wrappers.Monitor(env, aigym_path, force=True)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim)
policy = Policy(obs_dim, act_dim, kl_targ)
#policy.restore_weights() ## -------------
#val_func.restore_weights() ## -------------
# run a few episodes of untrained policy to initialize scaler:
run_policy(env, policy, scaler, logger, episodes=5)
episode = 0
while episode < num_episodes:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
episode += len(trajectories)
add_value(trajectories, val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
log_batch_stats(observes, actions, advantages, disc_sum_rew, logger,
episode)
policy.update(observes, actions, advantages, logger) # update policy
val_func.fit(observes, disc_sum_rew, logger) # update value function
logger.write(display=True) # write logger results to file and stdout
if killer.kill_now:
if input('Terminate training (y/[n])? ') == 'y':
break
killer.kill_now = False
print("Scaler vars,means: ")
print(scaler.vars, scaler.means)
for i in range(3):
run_episode(env, policy, scaler, animate=True)
#policy.save_weights()
#val_func.save_weights()
#WARNING: scaler is disabled
logger.close()
policy.close_sess()
val_func.close_sess()
def eval_models(env_name, num_episodes, gamma, lam, kl_targ, coef,
use_lr_adjust, ada_kl_penalty, seed, epochs, phi_epochs,
max_timesteps, reg_scale, phi_lr, phi_hs, policy_size, phi_obj,
load_model):
env, obs_dim, act_dim = init_gym(env_name)
set_global_seeds(seed)
env.seed(seed)
env._max_episode_steps = max_timesteps
obs_dim += 1
now = datetime.utcnow().strftime("%b-%d_%H:%M:%S")
aigym_path = os.path.join('log-files/', env_name, now)
env = wrappers.Monitor(env, aigym_path, force=True, video_callable=False)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim)
policy = Policy(obs_dim,
act_dim,
kl_targ,
epochs,
phi_epochs,
policy_size=policy_size,
phi_hidden_sizes=phi_hs,
reg_scale=reg_scale,
lr_phi=phi_lr,
phi_obj=phi_obj)
logger.log("loading model")
load_dir = "models/"
policy.load_model(load_dir)
val_func.load_val_model(load_dir)
run_policy(env, policy, scaler, num_episodes, max_timesteps=max_timesteps)
episode = 0
trajectories, traj_len_list = run_policy(env,
policy,
scaler,
num_episodes,
max_timesteps=max_timesteps)
num_traj = len(trajectories)
logger.log("Avg Length %d total Length %d"%( \
np.mean(traj_len_list), \
np.sum(traj_len_list)))
episode += len(trajectories)
add_value(trajectories, val_func)
add_disc_sum_rew(trajectories, gamma)
add_gae(trajectories, gamma, lam)
observes, actions, advantages, disc_sum_rew = build_train_set(trajectories)
sub_folder = "eval_data/%s_%s_data_seed=%d_max-steps=%d"%(\
env_name, phi_obj,
seed, max_timesteps)
if not os.path.exists(sub_folder):
os.mkdir(sub_folder)
# save original gradient
mc_grad_info = policy.get_batch_gradient(observes,
actions,
advantages,
c=0.)
mc_grad_info['traj_lens'] = traj_len_list
with open(sub_folder + '/mc_num_episode=%d.pkl' % (num_episodes),
'wb') as fp:
pickle.dump(mc_grad_info, fp)
policy.update(load_model,
observes,
actions,
advantages,
use_lr_adjust,
ada_kl_penalty,
c=1) # update policy
stein_grad_info = policy.get_batch_gradient(observes, \
actions, advantages, c=1.)
stein_grad_info['traj_lens'] = traj_len_list
with open(sub_folder + '/stein_num_episode=%d.pkl' % (num_episodes),
'wb') as fp:
pickle.dump(stein_grad_info, fp)
def main(env_name, num_episodes, gamma, lam, kl_targ, batch_size, restore_path,
out_path, thread_count, animation_mode, gait_name, gait_length,
gaits_config_path, reward_mask, log_rewards, gait_reward_weight,
g_colab, progress_reward_weight, phase_time_limit):
""" Main training loop
Args:
env_name: OpenAI Gym environment name, e.g. 'Hopper-v1'
num_episodes: maximum number of episodes to run
gamma: reward discount factor (float)
lam: lambda from Generalized Advantage Estimate
kl_targ: D_KL target for policy update [D_KL(pi_old || pi_new)
batch_size: number of episodes per policy training batch
"""
killer = GracefulKiller()
# restore_path = os.path.abspath(restore_path)
env, obs_dim, act_dim = init_gym(env_name)
log_rewards = log_rewards or (num_episodes == 0)
env_list = []
if thread_count > 1:
env_list, obs_dim, act_dim = init_gyms(env_name, batch_size)
obs_dim += 1 # add 1 to obs dimension for time step feature (see run_episode())
start_time = datetime.now() # create unique directories
start_time_str = start_time.strftime("%b-%d/%H.%M.%S")
logger = Logger(logname=env_name, now=start_time_str, out_path=out_path)
env.env.set_params(gaits_config_path=gaits_config_path,
gait_name=gait_name,
gait_cycle_len=gait_length,
out_path=logger.path,
log_rewards=log_rewards,
render_mode=animation_mode,
reward_mask=reward_mask,
contact_reward=gait_reward_weight,
g_colab=g_colab,
progress_weight=progress_reward_weight,
phase_time_limit=phase_time_limit)
scaler = Scaler(obs_dim)
val_func = NNValueFunction(obs_dim, logger, restore_path)
policy = Policy(obs_dim, act_dim, kl_targ, logger, restore_path)
log_train_info(logger, num_episodes, start_time_str, gait_name,
gait_length, batch_size, restore_path, reward_mask,
gait_reward_weight, progress_reward_weight,
phase_time_limit)
# run a few episodes of untrained policy to initialize scaler:
episode = 0
try:
if restore_path is None:
print("\nInitializing scaler (may take some time)... ")
run_policy(env, policy, scaler, logger, episodes=5)
print("Done\n")
else:
scaler.load(restore_path, obs_dim)
while episode < num_episodes:
sim_time = datetime.now()
if thread_count > 1:
trajectories = run_policy_parallel(env_list,
policy,
scaler,
logger,
episodes=batch_size,
thread_num=thread_count)
else:
trajectories = run_policy(env,
policy,
scaler,
logger,
episodes=batch_size)
sim_time = datetime.now() - sim_time
episode += len(trajectories)
add_value(trajectories,
val_func) # add estimated values to episodes
add_disc_sum_rew(trajectories,
gamma) # calculated discounted sum of Rs
add_gae(trajectories, gamma, lam) # calculate advantage
# concatenate all episodes into single NumPy arrays
observes, actions, advantages, disc_sum_rew = build_train_set(
trajectories)
# add various stats to training log:
train_time = datetime.now() - start_time
policy_time = datetime.now()
policy.update(observes, actions, advantages,
logger) # update policy
policy_time = datetime.now() - policy_time
val_time = datetime.now()
val_func.fit(observes, disc_sum_rew,
logger) # update value function
val_time = datetime.now() - val_time
log_batch_stats(observes, actions, advantages, disc_sum_rew,
logger, episode, train_time, sim_time, policy_time,
val_time)
logger.write(
display=True) # write logger results to file and stdout
print("Estimated time left: {}\n".format(
estimate_time_left(episode, num_episodes, train_time)))
if episode % 1000 == 0:
policy.save()
val_func.save()
scaler.save(logger.path)
print("Data saved at {}\n".format(logger.path))
update_train_info(logger, episode)
if animation_mode > 0:
run_policy(env,
policy,
scaler,
logger,
episodes=1,
animate=True,
anim_name='epizode_{}'.format(episode))
if episode % 5000 == 0:
os.rename(
os.path.join(logger.path, 'value_dump'),
os.path.join(logger.path, 'value_dump_' + str(episode)))
os.rename(
os.path.join(logger.path, 'policy_dump'),
os.path.join(logger.path, 'policy_dump_' + str(episode)))
# if episode == 20000:
# reward_mask = 63
# env.env.set_params(gaits_config_path=gaits_config_path, gait_name=gait_name, gait_cycle_len=gait_length,
# out_path=logger.path, log_rewards=log_rewards, render_mode=animation_mode,
# reward_mask=reward_mask, contact_reward=gait_reward_weight, g_colab=g_colab)
print("Progress Enabled")
if killer.kill_now:
# if input('Terminate training (y/[n])? ') == 'y':
# break
# killer.kill_now = False
break
finally:
if animation_mode > 0 or num_episodes == 0:
print("Rendering result video")
try:
trajectories = run_policy(
env,
policy,
scaler,
logger,
episodes=1,
animate=True,
anim_name='final_epizode_{}'.format(episode))
# for walk analysis
for t in trajectories:
logger.log_trajectory(t)
except Exception as e:
print("Failed to animate results, error: {}".format(e))
raise e
scaler.save(logger.path)
policy.close_sess()
val_func.close_sess()
update_train_info(logger, episode)
logger.close()