def f(ppo_policy, config, conditions):
# env to generate fake state
env = gym.make(config.env)
env = BBallWrapper(env,
init_mode=3,
fps=config.FPS,
if_back_real=config.if_back_real,
time_limit=config.max_length)
env = MonitorWrapper(
env,
directory=os.path.join(config.logdir, 'gail_training/'),
if_back_real=config.if_back_real,
# init from dataset in order
init_mode=3)
# align the conditions with env
# -1 : newest state
conditions = conditions[None]
env.data = conditions[:, :, -1]
obs_state = env.reset()
one_epi_fake = []
one_epi_fake_act = []
for len_idx in range(config.max_length):
if config.if_back_real:
act = ppo_policy.act(np.array(conditions[:,
len_idx:len_idx + 1, :]),
stochastic=True)
else:
act = ppo_policy.act(np.array(obs_state)[None, None],
stochastic=True)
transformed_act = [
# Discrete(3) must be int
int(0),
# Box(2,)
np.array([0.0, 0.0], dtype=np.float32),
# Box(5, 2)
np.zeros(shape=[5, 2], dtype=np.float32),
# Box(5, 2)
np.reshape(act, [5, 2])
]
obs_state, _, _, _ = env.step(transformed_act)
one_epi_fake.append(obs_state[-1])
one_epi_fake_act.append(act.reshape([5, 2]))
return one_epi_fake, one_epi_fake_act
def train(config, env_processes, outdir):
""" Training and evaluation entry point yielding scores.
Resolves some configuration attributes, creates environments, graph, and
training loop. By default, assigns all operations to the CPU.
Args
----
config : Object providing configurations via attributes.
env_processes : Whether to step environment in external processes.
outdir : Directory path to save rendering result while traning.
Yields
------
score : Evaluation scores.
"""
tf.reset_default_graph()
# env to get config
dummy_env = gym.make(config.env)
def normalize_observ(observ):
min_ = dummy_env.observation_space.low[0]
max_ = dummy_env.observation_space.high[0]
observ = 2.0 * (observ - min_) / (max_ - min_) - 1.0
return observ
def normalize_action(act):
min_ = dummy_env.action_space[3].low
max_ = dummy_env.action_space[3].high
act = 2.0 * (act - min_) / (max_ - min_) - 1.0
return act
def denormalize_observ(observ):
min_ = dummy_env.observation_space.low[0]
max_ = dummy_env.observation_space.high[0]
observ = (observ + 1.0) * (max_ - min_) / 2.0 + min_
return observ
# env to testing
vanilla_env = gym.make(config.env)
vanilla_env = BBallWrapper(vanilla_env, init_mode=1, fps=config.FPS, if_back_real=False,
time_limit=50)
vanilla_env = MonitorWrapper(vanilla_env, directory=os.path.join(config.logdir, 'gail_testing_{}/'.format(config.train_len)), if_back_real=False, video_callable=lambda _: True,
# init from dataset
init_mode=1)
# if not os.path.exists(os.path.join(config.logdir, 'gail_testing')):
# os.makedirs(os.path.join(config.logdir, 'gail_testing'))
vanilla_env.data = np.load('bball_strategies/data/GAILEnvData_51.npy')
# env to generate fake state
env = gym.make(config.env)
env = BBallWrapper(env, init_mode=3, fps=config.FPS, if_back_real=config.if_back_real,
time_limit=config.max_length)
env = MonitorWrapper(env, directory=os.path.join(config.logdir, 'gail_training/'), if_back_real=config.if_back_real,
# init from dataset in order
init_mode=3)
# Discriminator graph
with tf.device('/gpu:0'):
D = Discriminator(config, dummy_env)
# PPO graph
if config.update_every % config.num_agents:
tf.logging.warn('Number of agents should divide episodes per update.')
with tf.device('/cpu:0'):
batch_env = utility.define_batch_env(
lambda: _create_environment(config),
config.num_agents, env_processes, outdir=outdir, is_gail=config.is_gail)
graph = utility.define_simulation_graph(
batch_env, config.algorithm, config)
loop = _define_loop(
graph, config.logdir,
config.update_every * config.max_length,
config.eval_episodes * config.max_length)
total_steps = int(
config.steps / config.update_every *
(config.update_every + config.eval_episodes))
# Agent to genrate acttion
ppo_policy = PPOPolicy(config, env)
# Data
all_data = h5py.File(
'bball_strategies/data/GAILTransitionData_{}.hdf5'.format(config.train_len), 'r')
expert_data, valid_expert_data = np.split(
all_data['OBS'].value, [all_data['OBS'].value.shape[0]*9//10])
expert_action, valid_expert_action = np.split(
all_data['DEF_ACT'].value, [all_data['DEF_ACT'].value.shape[0]*9//10])
print('expert_data', expert_data.shape)
print('valid_expert_data', valid_expert_data.shape)
print('expert_action', expert_action.shape)
print('valid_expert_action', valid_expert_action.shape)
# TF Session
# TODO _num_finished_episodes => Variable:0
saver = utility.define_saver(
exclude=(r'.*_temporary.*', r'.*memory.*', r'Variable:0', r'.*Adam.*', r'.*beta.*'))
sess_config = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=config.log_device_placement)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
utility.initialize_variables(
sess, saver, config.logdir, resume=FLAGS.resume)
# NOTE reset variables in optimizer
D.reset_optimizer(sess)
# reset PPO optimizer
opt_reset = tf.group(
[v.initializer for v in graph.algo._optimizer.variables()])
sess.run(opt_reset)
# visulization stuff
if FLAGS.tally_only:
tally_reward_line_chart(config, sess.run(
D._global_steps), ppo_policy, D, denormalize_observ, normalize_observ, normalize_action)
exit()
# GAIL
cumulate_steps = sess.run(graph.step)
episode_idx = 0
valid_episode_idx = 0
while True:
if episode_idx > (expert_data.shape[0]-config.episodes_per_batch*config.train_d_per_ppo) or episode_idx == 0:
episode_idx = 0
perm_idx = np.random.permutation(expert_data.shape[0])
expert_data = expert_data[perm_idx]
expert_action = expert_action[perm_idx]
if valid_episode_idx > (valid_expert_data.shape[0]-config.episodes_per_batch) or valid_episode_idx == 0:
valid_episode_idx = 0
valid_perm_idx = np.random.permutation(
valid_expert_data.shape[0])
valid_expert_data = valid_expert_data[valid_perm_idx]
valid_expert_action = valid_expert_action[valid_perm_idx]
# testing
if valid_episode_idx % (100 * config.episodes_per_batch) == 0:
test_policy(config, vanilla_env, sess.run(D._global_steps), ppo_policy,
D, denormalize_observ)
if valid_episode_idx % (1000 * config.episodes_per_batch) == 0:
tally_reward_line_chart(config, sess.run(
D._global_steps), ppo_policy, D, denormalize_observ, normalize_observ, normalize_action)
# train Discriminator
train_Discriminator(
episode_idx, config, expert_data, expert_action, env, ppo_policy, D, normalize_observ, normalize_action)
if valid_episode_idx % (1000 * config.episodes_per_batch) == 0:
tally_reward_line_chart(config, sess.run(
D._global_steps), ppo_policy, D, denormalize_observ, normalize_observ, normalize_action)
# valid Discriminator
valid_Discriminator(
valid_episode_idx, config, valid_expert_data, valid_expert_action, env, ppo_policy, D, normalize_observ, normalize_action)
episode_idx += config.episodes_per_batch*config.train_d_per_ppo
valid_episode_idx += config.episodes_per_batch
# train PPO
print('train PPO')
cumulate_steps += total_steps
for score in loop.run(sess, saver, cumulate_steps):
yield score
batch_env.close()
vanilla_env.close()
env.close()
def tally_reward_line_chart(config, steps, ppo_policy, D, denormalize_observ, normalize_observ, normalize_action):
""" tally 100 episodes as line chart to show how well the discriminator judge on each state of real and fake episode
"""
if config.is_gail:
episode_amount = 100
# real data
all_data = h5py.File(
'bball_strategies/data/GAILTransitionData_51.hdf5', 'r')
expert_data, _ = np.split(
all_data['OBS'].value, [all_data['OBS'].value.shape[0]*9//10])
expert_action, _ = np.split(
all_data['DEF_ACT'].value, [all_data['DEF_ACT'].value.shape[0]*9//10])
# env
vanilla_env = gym.make(config.env)
vanilla_env = BBallWrapper(vanilla_env, init_mode=1, fps=config.FPS, if_back_real=False,
time_limit=50)
vanilla_env.data = np.load('bball_strategies/data/GAILEnvData_51.npy')
# real
selected_idx = np.random.choice(expert_data.shape[0], episode_amount)
# frame 0 is condition
batch_real_states = expert_data[selected_idx, 1:]
real_action = expert_action[selected_idx, :-1]
batch_real_states = np.concatenate(
batch_real_states, axis=0)
real_action = np.concatenate(real_action[:, None], axis=0)
batch_real_states = normalize_observ(batch_real_states)
real_rewards = D.get_rewards_value(
batch_real_states, normalize_action(real_action)).reshape([100, -1])
# fake
numpy_collector = []
act_collector = []
for _ in range(episode_amount):
vanilla_obs = vanilla_env.reset()
for _ in range(vanilla_env.time_limit):
vanilla_act = ppo_policy.act(
np.array(vanilla_obs)[None, None], stochastic=False)
act_collector.append(vanilla_act.reshape([1, 5, 2]))
vanilla_trans_act = [
# Discrete(3) must be int
int(0),
# Box(2,)
np.array([0.0, 0.0], dtype=np.float32),
# Box(5, 2)
np.zeros(shape=[5, 2], dtype=np.float32),
# Box(5, 2)
np.reshape(vanilla_act, [5, 2])
]
vanilla_obs, _, _, _ = vanilla_env.step(
vanilla_trans_act)
numpy_collector.append(vanilla_obs)
numpy_collector = np.array(numpy_collector)
act_collector = np.array(act_collector)
fake_rewards = D.get_rewards_value(
numpy_collector, act_collector).reshape([100, -1])
# vis
vis_line_chart(real_rewards, fake_rewards, config.logdir, str(steps))
else:
episode_amount = 100
# real data
all_data = h5py.File(
'bball_strategies/data/GAILTransitionData_51.hdf5', 'r')
expert_data, _ = np.split(
all_data['OBS'].value, [all_data['OBS'].value.shape[0]*9//10])
expert_action, _ = np.split(
all_data['DEF_ACT'].value, [all_data['DEF_ACT'].value.shape[0]*9//10])
# env
vanilla_env = gym.make(config.env)
vanilla_env = BBallWrapper(vanilla_env, init_mode=1, fps=config.FPS, if_back_real=False,
time_limit=config.max_length)
vanilla_env.data = np.load('bball_strategies/data/GAILEnvData_51.npy')
# real
selected_idx = np.random.choice(expert_data.shape[0], episode_amount)
# frame 0 is condition
batch_real_states = expert_data[selected_idx, 1:config.max_length+1, -1]
real_action = expert_action[selected_idx, :config.max_length]
batch_real_states = normalize_observ(batch_real_states)
real_rewards = D.get_rewards_value(
batch_real_states, normalize_action(real_action)).reshape([-1, 1])
real_rewards = np.tile(real_rewards, [1, config.max_length])
# fake
numpy_collector = []
act_collector = []
for _ in range(episode_amount):
vanilla_obs = vanilla_env.reset()
epi_obs = []
epi_act = []
for _ in range(config.max_length):
vanilla_act = ppo_policy.act(
np.array(vanilla_obs)[None, None], stochastic=False)
vanilla_trans_act = [
# Discrete(3) must be int
int(0),
# Box(2,)
np.array([0.0, 0.0], dtype=np.float32),
# Box(5, 2)
np.zeros(shape=[5, 2], dtype=np.float32),
# Box(5, 2)
np.reshape(vanilla_act, [5, 2])
]
vanilla_obs, _, _, _ = vanilla_env.step(
vanilla_trans_act)
epi_obs.append(vanilla_obs[-1])
epi_act.append(vanilla_act.reshape([5, 2]))
numpy_collector.append(epi_obs)
act_collector.append(epi_act)
numpy_collector = np.array(numpy_collector)
act_collector = np.array(act_collector)
fake_rewards = D.get_rewards_value(
numpy_collector, act_collector).reshape([-1, 1])
fake_rewards = np.tile(fake_rewards, [1, config.max_length])
# vis
vis_line_chart(real_rewards, fake_rewards, config.logdir, str(steps))
def train(config, env_processes, outdir):
""" Training and evaluation entry point yielding scores.
Resolves some configuration attributes, creates environments, graph, and
training loop. By default, assigns all operations to the CPU.
Args
----
config : Object providing configurations via attributes.
env_processes : Whether to step environment in external processes.
outdir : Directory path to save rendering result while traning.
Yields
------
score : Evaluation scores.
"""
tf.reset_default_graph()
# env to get config
dummy_env = gym.make(config.env)
def normalize_observ(observ):
min_ = dummy_env.observation_space.low[0]
max_ = dummy_env.observation_space.high[0]
observ = 2.0 * (observ - min_) / (max_ - min_) - 1.0
return observ
def normalize_action(act):
min_ = dummy_env.action_space[3].low
max_ = dummy_env.action_space[3].high
act = 2.0 * (act - min_) / (max_ - min_) - 1.0
return act
def denormalize_observ(observ):
min_ = dummy_env.observation_space.low[0]
max_ = dummy_env.observation_space.high[0]
observ = (observ + 1.0) * (max_ - min_) / 2.0 + min_
return observ
# env to testing
vanilla_env = gym.make(config.env)
vanilla_env = BBallWrapper(vanilla_env, init_mode=1, fps=config.FPS, if_back_real=False,
time_limit=50)
vanilla_env = MonitorWrapper(vanilla_env, directory=os.path.join(config.logdir, 'gail_testing_G{}_D{}/'.format(config.train_len, config.D_len)), if_back_real=False, video_callable=lambda _: True,
# init from dataset
init_mode=1)
vanilla_env.data = np.load('bball_strategies/data/GAILEnvData_51.npy')
# env to generate fake state
env = gym.make(config.env)
env = BBallWrapper(env, init_mode=3, fps=config.FPS, if_back_real=config.if_back_real,
time_limit=config.max_length)
env = MonitorWrapper(env, directory=os.path.join(config.logdir, 'gail_training/'), if_back_real=config.if_back_real,
# init from dataset in order
init_mode=3)
# PPO graph
if config.update_every % config.num_agents:
tf.logging.warn('Number of agents should divide episodes per update.')
with tf.device('/cpu:0'):
batch_env = utility.define_batch_env(
lambda: _create_environment(config),
config.num_agents, env_processes, outdir=outdir, is_gail=config.is_gail)
graph = utility.define_simulation_graph(
batch_env, config.algorithm, config)
loop = _define_loop(
graph, config.logdir,
config.update_every * config.max_length,
config.eval_episodes * config.max_length)
total_steps = int(
config.steps / config.update_every *
(config.update_every + config.eval_episodes))
# Agent to genrate acttion
ppo_policy = PPOPolicy(config, env)
# Data
all_data = h5py.File(
'bball_strategies/data/GAILTransitionData_{}.hdf5'.format(config.train_len), 'r')
expert_data, valid_expert_data = np.split(
all_data['OBS'].value, [all_data['OBS'].value.shape[0] * 9 // 10])
expert_action, valid_expert_action = np.split(
all_data['DEF_ACT'].value, [all_data['DEF_ACT'].value.shape[0] * 9 // 10])
print('expert_data', expert_data.shape)
print('valid_expert_data', valid_expert_data.shape)
print('expert_action', expert_action.shape)
print('valid_expert_action', valid_expert_action.shape)
# Preprocessing/ Normalization
expert_data = normalize_observ(expert_data)
valid_expert_data = normalize_observ(valid_expert_data)
expert_action = normalize_action(expert_action)
valid_expert_action = normalize_action(valid_expert_action)
# summary writer of Discriminator
summary_writer = tf.summary.FileWriter(config.logdir + '/Disciminator')
# TF Session
# TODO _num_finished_episodes => Variable:0
saver = utility.define_saver(
exclude=(r'.*_temporary.*', r'.*memory.*', r'Variable:0', r'.*Adam.*', r'.*beta.*'))
sess_config = tf.ConfigProto(
allow_soft_placement=True, log_device_placement=config.log_device_placement)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
utility.initialize_variables(
sess, saver, config.logdir, resume=FLAGS.resume)
# NOTE reset variables in optimizer
# opt_reset_D = tf.group(
# [v.initializer for v in graph.algo.D.optimizer.variables()])
# # reset PPO optimizer
# opt_reset = tf.group(
# [v.initializer for v in graph.algo._optimizer.variables()])
# sess.run([opt_reset, opt_reset_D])
# visulization stuff
if FLAGS.tally_only:
tally_reward_line_chart(config, sess.run(
graph.algo.D._steps), ppo_policy, D, denormalize_observ, normalize_observ, normalize_action)
exit()
# GAIL
cumulate_steps = sess.run(graph.step)
episode_idx = 0
while True:
if episode_idx > (expert_data.shape[0] - config.episodes_per_batch * config.train_d_per_ppo) or episode_idx == 0:
episode_idx = 0
perm_idx = np.random.permutation(expert_data.shape[0])
expert_data = expert_data[perm_idx]
expert_action = expert_action[perm_idx]
# # testing
if episode_idx % (config.train_d_per_ppo * 100 * config.episodes_per_batch) == 0:
test_policy(config, vanilla_env, sess.run(graph.algo.D._steps), ppo_policy,
graph.algo.D, denormalize_observ)
if episode_idx % (config.train_d_per_ppo * 1000 * config.episodes_per_batch) == 0:
tally_reward_line_chart(config, sess.run(
graph.algo.D._steps), ppo_policy, graph.algo.D, denormalize_observ, normalize_observ, normalize_action)
# # train Discriminator
gail_timer = time.time()
for _ in range(config.train_d_per_ppo):
if config.is_double_curiculum:
observ = expert_data[episode_idx:episode_idx +config.episodes_per_batch, 1:]
action = expert_action[episode_idx:episode_idx+config.episodes_per_batch, :-1]
if config.use_padding:
# 1. padding with buffer
buffer = observ[:, 0, :-1]
padded_observ = np.concatenate([buffer, observ[:, :, -1]], axis=1)
padded_act = np.concatenate([np.zeros(shape=[action.shape[0], 9, 5, 2]), action], axis=1)
# 2. split the whole episode into training data of Discriminator with length=config.D_len
training_obs = []
training_act = []
for i in range(config.max_length-config.D_len+10):
training_obs.append(padded_observ[:, i:i+config.D_len])
training_act.append(padded_act[:, i:i+config.D_len])
training_obs = np.concatenate(training_obs, axis=0)
training_act = np.concatenate(training_act, axis=0)
else:
pass
else:
training_obs = expert_data[episode_idx:episode_idx +config.episodes_per_batch, 1:, -1]
training_act = expert_action[episode_idx:episode_idx+config.episodes_per_batch, :-1]
feed_dict = {
graph.is_training: True,
graph.should_log: True,
graph.do_report: True,
graph.force_reset: False,
graph.algo.D._expert_s: training_obs,
graph.algo.D._expert_a: training_act}
gail_counter = 0
while gail_counter < config.gail_steps:
gail_summary = sess.run(
graph.gail_summary, feed_dict=feed_dict)
if gail_summary:
summary_writer.add_summary(
gail_summary, global_step=sess.run(graph.algo.D._steps))
gail_counter += 1
episode_idx += config.episodes_per_batch
print('Time Cost of Discriminator per Update: {}'.format(
(time.time() - gail_timer) / config.train_d_per_ppo))
# train ppo
cumulate_steps += total_steps
for score in loop.run(sess, saver, cumulate_steps):
yield score
batch_env.close()
vanilla_env.close()
env.close()
