def vis_data(off_data, off_label, def_data, def_label, outdir, start_idx=0):
""" vis the dataset of transitions
Args
----
"""
idx_ = start_idx
init_pos = [
np.array(off_data[idx_, 0, -1, 0, :]),
np.array(off_data[idx_, 0, -1, 1:6, :], dtype=np.float),
np.array(off_data[idx_, 0, -1, 6:11, :], dtype=np.float)
]
env = gym.make('bball-pretrain-v0')
env = BBallWrapper(env,
if_clip=False,
if_norm_obs=False,
if_norm_act=False,
init_mode=2,
if_vis_visual_aid=True,
if_vis_trajectory=False,
init_positions=init_pos)
env = gym.wrappers.Monitor(env,
outdir,
lambda unused_episode_number: True,
force=False,
resume=True)
obs = env.reset()
while True:
# prevent from modification
temp_off_label = np.array(off_label[idx_, 0])
temp_def_label = np.array(def_label[idx_, 0])
if idx_ == start_idx:
# the env's velocity is zero, so we add the last velocity after env reset.
last_vel = off_data[idx_, 0, -1, 1:6, :] - \
off_data[idx_, 0, -2, 1:6, :]
temp_off_label[5:] += last_vel.reshape([
10,
])
last_vel = def_data[idx_, 0, -1, 6:11, :] - \
def_data[idx_, 0, -2, 6:11, :]
temp_def_label += last_vel.reshape([
10,
])
# offense
action = pack_action([temp_off_label[:3], temp_off_label[3:]],
team='offense')
obs, _, done, _ = env.step(action)
if done:
env.close()
break
# deffense
action = pack_action(temp_def_label, team='defense')
obs, _, done, _ = env.step(action)
if done:
env.close()
break
idx_ += 1
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 collect_results(config, steps, ppo_policy, D, denormalize_observ, generated_amount=100):
""" test policy
- draw episode into mpeg video
- collect episode with scores on each frame into .npz file (for out customized player)
Args
-----
config : object, providing configurations via attributes.
vanilla_env : object, env
steps : int, to name the file with number of iterations of Discriminator
ppo_policy : object, policy to generate actions
D : object, discriminator to judge realistic
denormalize_observ : function, denorm the returned observation
"""
timer = time.time()
# read condition length
data_len = np.load('bball_strategies/data/FixedFPS5Length.npy')
# data_len = np.load('bball_strategies/data/WGAN/all_model_results/length.npy')
# env to testing
vanilla_env = gym.make(config.env)
vanilla_env = BBallWrapper(vanilla_env, data=h5py.File(
'bball_strategies/data/OrderedGAILTransitionData_Testing.hdf5', 'r'), init_mode=1, fps=config.FPS, time_limit=np.max(data_len)-2)
vanilla_env = MonitorWrapper(vanilla_env, directory=os.path.join(config.logdir, 'collect_result/video/'), video_callable=lambda _: True,
# init from dataset
init_mode=1)
total_output = []
index_list = []
for i in range(generated_amount):
print('generating # {} episode'.format(i))
numpy_collector = []
act_collector = []
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([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, _, _, info = vanilla_env.step(
vanilla_trans_act)
numpy_collector.append(vanilla_obs)
index_list.append(info['data_idx'])
numpy_collector = np.array(numpy_collector)
act_collector = np.array(act_collector)
numpy_collector = denormalize_observ(numpy_collector)
total_output.append(numpy_collector)
total_output = np.array(total_output)
# save numpy
np.save(os.path.join(config.logdir,
'collect_result/total_output.npy'), total_output)
np.save(os.path.join(config.logdir,
'collect_result/total_output_length.npy'), data_len[index_list]-2)
print('collect_results time cost: {} per episode'.format(
(time.time() - timer)/generated_amount))
vanilla_env.close()
def vis_result(sess, model, off_data, off_label, def_data, def_label, outdir,
num_video):
""" vis the results by using the pretrain output interacting with env
Args
----
"""
data_len = np.load('bball_strategies/data/FixedFPS5Length.npy')
accumulator = 0
for i, v in enumerate(data_len):
data_len[i] += accumulator
accumulator += v
for i in range(num_video):
start_idx = data_len[i]
idx_ = start_idx
init_pos = [
np.array(off_data[idx_, 0, -1, 0, :]),
np.array(off_data[idx_, 0, -1, 1:6, :], dtype=np.float),
np.array(off_data[idx_, 0, -1, 6:11, :], dtype=np.float)
]
env = gym.make('bball-pretrain-v0')
env = BBallWrapper(env,
if_clip=False,
if_norm_obs=False,
if_norm_act=False,
init_mode=2,
if_vis_visual_aid=True,
if_vis_trajectory=False,
init_positions=init_pos)
env = gym.wrappers.Monitor(env,
outdir,
lambda unused_episode_number: True,
force=False,
resume=True)
obs = env.reset()
while True:
# prevent from modification
temp_off_label = np.array(off_label[idx_, 0])
temp_def_label = np.array(def_label[idx_, 0])
if idx_ == start_idx:
# the env's velocity is zero, so we add the last velocity after env reset.
last_vel = off_data[idx_, 0, -1, 1:6, :] - \
off_data[idx_, 0, -2, 1:6, :]
temp_off_label[5:] += last_vel.reshape([
10,
])
last_vel = def_data[idx_, 0, -1, 6:11, :] - \
def_data[idx_, 0, -2, 6:11, :]
temp_def_label += last_vel.reshape([
10,
])
if FLAGS.config == 'offense':
# offense turn
obs = norm_obs(env, obs)
logits, actions = model.perform(sess, obs[None, None])
actions = pack_action([logits[0, 0], actions[0, 0]],
FLAGS.config)
obs, _, done, _ = env.step(actions)
if done:
env.close()
break
# defense turn
actions = pack_action(temp_def_label, team='defense')
obs, _, done, _ = env.step(actions)
if done:
env.close()
break
elif FLAGS.config == 'defense':
# offense turn
actions = pack_action([temp_off_label[:3], temp_off_label[3:]],
team='offense')
obs, _, done, _ = env.step(actions)
if done:
env.close()
break
# defense turn
obs = norm_obs(env, obs)
actions = model.perform(sess, obs[None, None])
actions = pack_action(actions, FLAGS.config)
obs, _, done, _ = env.step(actions)
if done:
env.close()
break
idx_ += 1
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 tally_reward_line_chart(config, steps, ppo_policy, D, normalize_observ, normalize_action, stochastic=False):
""" tally a line chart by 100 episodes to show how well the discriminator judge on each state
Args
-----
config : object, providing configurations via attributes.
steps : int, to name the file with number of iterations of Discriminator
ppo_policy : object, policy to generate actions
D : object, discriminator to judge realistic
normalize_observ : function, norm the states of real data
normalize_action : function, norm the actions of real data
stochastic : bool, decide the methods to generate actions, True->sampled, False->select mode
"""
timer = time.time()
episode_amount = 100
# env
vanilla_env = gym.make(config.env)
vanilla_env = BBallWrapper(vanilla_env, data=h5py.File('bball_strategies/data/OrderedGAILTransitionData_522.hdf5', 'r'), init_mode=1, fps=config.FPS, time_limit=50)
# fake
numpy_collector = []
act_collector = []
real_numpy_collector = []
real_act_collector = []
for _ in range(episode_amount):
vanilla_obs = vanilla_env.reset()
epi_obs = []
epi_act = []
real_epi_obs = []
real_epi_act = []
for _ in range(vanilla_env.time_limit):
vanilla_act = ppo_policy.act(
np.array(vanilla_obs)[None, None], stochastic=stochastic)
vanilla_trans_act = [
# Discrete(3) must be int
int(0),
# Box(2,), ball
np.array([0.0, 0.0], dtype=np.float32),
# Box(5, 2), offense
np.zeros(shape=[5, 2], dtype=np.float32),
# Box(5, 2), defense
np.reshape(vanilla_act, [5, 2])
]
vanilla_obs, _, _, info = vanilla_env.step(
vanilla_trans_act)
epi_obs.append(vanilla_obs[-1])
epi_act.append(vanilla_act.reshape([5, 2]))
real_epi_obs.append(np.concatenate(
[vanilla_obs[-1, 0:6], normalize_observ(info['expert_s']), vanilla_obs[-1, 11:14]], axis=0))
real_epi_act.append(info['expert_a'])
numpy_collector.append(epi_obs)
act_collector.append(epi_act)
real_numpy_collector.append(real_epi_obs)
real_act_collector.append(real_epi_act)
numpy_collector = np.array(numpy_collector)
act_collector = np.array(act_collector)
real_numpy_collector = np.array(real_numpy_collector)
real_act_collector = np.array(real_act_collector)
fake_rewards = D.get_rewards_value(
numpy_collector, act_collector)
real_act_collector = normalize_action(real_act_collector)
real_rewards = D.get_rewards_value(
real_numpy_collector, real_act_collector)
# vis
if stochastic:
save_path = os.path.join(config.logdir, 'gail_testing_G{}_D{}'.format(
config.max_length, config.D_len), 'line_chart_stochastic/')
else:
save_path = os.path.join(config.logdir, 'gail_testing_G{}_D{}'.format(
config.max_length, config.D_len), 'line_chart/')
vis_line_chart(real_rewards, fake_rewards, save_path, str(steps))
print('tally_reward_line_chart time cost: {}'.format(time.time() - timer))
