def main():
FLAGS.set_seed()
FLAGS.freeze()
env = create_env(FLAGS.env.id,
FLAGS.seed,
rescale_action=FLAGS.env.rescale_action)
dim_state = env.observation_space.shape[0]
dim_action = env.action_space.shape[0]
subsampling_rate = env.max_episode_steps // FLAGS.GAIL.trajectory_size
# load expert dataset
set_random_seed(2020)
expert_dataset = load_expert_dataset(FLAGS.GAIL.buf_load)
expert_state = np.stack([t.obs for t in expert_dataset.buffer()])
expert_next_state = np.stack([t.next_obs for t in expert_dataset.buffer()])
expert_done = np.stack([t.done for t in expert_dataset.buffer()])
np.testing.assert_allclose(
expert_next_state[:-1] * (1 - expert_done[:-1][:, None]),
expert_state[1:] * (1 - expert_done[:-1][:, None]))
del expert_state, expert_next_state, expert_done
expert_reward = expert_dataset.get_average_reward()
logger.info('Expert Reward %f', expert_reward)
if FLAGS.GAIL.learn_absorbing:
expert_dataset.add_absorbing_states(env)
eval_batch = expert_dataset.sample(1024)
eval_state = np.stack([t.obs for t in eval_batch])
eval_action = np.stack([t.action for t in eval_batch])
eval_next_state = np.stack([t.next_obs for t in eval_batch])
logger.info('Sampled obs: %.4f, acs: %.4f', np.mean(eval_state),
np.mean(eval_action))
expert_dataset.subsample_trajectories(FLAGS.GAIL.traj_limit)
logger.info('Original dataset size {}'.format(len(expert_dataset)))
expert_dataset.subsample_transitions(subsampling_rate)
logger.info('Subsampled dataset size {}'.format(len(expert_dataset)))
logger.info('np random: %d random : %d', np.random.randint(1000),
random.randint(0, 1000))
set_random_seed(FLAGS.seed)
# expert actor
actor = Actor(dim_state,
dim_action,
hidden_sizes=FLAGS.SAC.actor_hidden_sizes)
# generator
normalizers = Normalizers(dim_action=dim_action, dim_state=dim_state)
policy = GaussianMLPPolicy(dim_state,
dim_action,
FLAGS.TRPO.policy_hidden_sizes,
output_diff=FLAGS.TRPO.output_diff,
normalizers=normalizers)
vfn = MLPVFunction(dim_state, dim_action, FLAGS.TRPO.vf_hidden_sizes,
normalizers.state)
algo = TRPO(vfn=vfn,
policy=policy,
dim_state=dim_state,
dim_action=dim_action,
**FLAGS.TRPO.algo.as_dict())
subsampling_rate = env.max_episode_steps // FLAGS.GAIL.trajectory_size
if FLAGS.GAIL.reward_type == 'nn':
expert_batch = expert_dataset.buffer()
expert_state = np.stack([t.obs for t in expert_batch])
loc, scale = np.mean(expert_state, axis=0,
keepdims=True), np.std(expert_state,
axis=0,
keepdims=True)
del expert_batch, expert_state
logger.info('loc = {}\nscale={}'.format(loc, scale))
discriminator = Discriminator(dim_state,
dim_action,
normalizers=normalizers,
subsampling_rate=subsampling_rate,
loc=loc,
scale=scale,
**FLAGS.GAIL.discriminator.as_dict())
else:
raise NotImplementedError
bc_loss = BehavioralCloningLoss(dim_state,
dim_action,
policy,
lr=FLAGS.BC.lr,
train_std=FLAGS.BC.train_std)
tf.get_default_session().run(tf.global_variables_initializer())
loader = nn.ModuleDict({'actor': actor})
loader.load_state_dict(
np.load(FLAGS.ckpt.policy_load, allow_pickle=True)[()])
logger.info('Load policy from %s' % FLAGS.ckpt.policy_load)
saver = nn.ModuleDict({
'policy': policy,
'vfn': vfn,
'normalizers': normalizers,
'discriminator': discriminator
})
print(saver)
# updater normalizer
expert_state = np.stack([t.obs for t in expert_dataset.buffer()])
expert_action = np.stack([t.action for t in expert_dataset.buffer()])
expert_next_state = np.stack([t.next_obs for t in expert_dataset.buffer()])
normalizers.state.update(expert_state)
normalizers.action.update(expert_action)
normalizers.diff.update(expert_next_state - expert_state)
del expert_state, expert_action, expert_next_state
eval_gamma = 0.999
eval_returns, eval_lengths = evaluate_on_true_env(actor,
env,
gamma=eval_gamma)
logger.warning(
'Test policy true value = %.4f true length = %d (gamma = %f)',
np.mean(eval_returns), np.mean(eval_lengths), eval_gamma)
# pretrain
for n_updates in range(FLAGS.GAIL.pretrain_iters):
expert_batch = expert_dataset.sample(FLAGS.BC.batch_size)
expert_state = np.stack([t.obs for t in expert_batch])
expert_action = np.stack([t.action for t in expert_batch])
expert_next_state = np.stack([t.next_obs for t in expert_batch])
_, loss, grad_norm = bc_loss.get_loss(expert_state,
expert_action,
expert_next_state,
fetch='train loss grad_norm')
if n_updates % 100 == 0:
mse_loss = policy.get_mse_loss(expert_state, expert_action,
expert_next_state)
logger.info(
'[Pretrain] iter = %d grad_norm = %.4f loss = %.4f mse_loss = %.4f',
n_updates, grad_norm, loss, mse_loss)
# virtual env
virtual_env = VirtualEnv(policy,
env,
n_envs=FLAGS.env.num_env,
stochastic_model=True)
virtual_runner = VirtualRunner(virtual_env,
max_steps=env.max_episode_steps,
gamma=FLAGS.TRPO.gamma,
lambda_=FLAGS.TRPO.lambda_,
rescale_action=False)
env_eval_stochastic = VirtualEnv(policy,
env,
n_envs=4,
stochastic_model=True)
env_eval_deterministic = VirtualEnv(policy,
env,
n_envs=4,
stochastic_model=False)
max_ent_coef = FLAGS.TRPO.algo.ent_coef
true_return = np.mean(eval_returns)
for t in range(0, FLAGS.GAIL.total_timesteps,
FLAGS.TRPO.rollout_samples * FLAGS.GAIL.g_iters):
time_st = time.time()
if t % FLAGS.GAIL.eval_freq == 0:
eval_returns_stochastic, eval_lengths_stochastic = evaluate_on_virtual_env(
actor, env_eval_stochastic, gamma=eval_gamma)
eval_returns_deterministic, eval_lengths_deterministic = evaluate_on_virtual_env(
actor, env_eval_deterministic, gamma=eval_gamma)
log_kvs(
prefix='Evaluate',
kvs=dict(
iter=t,
stochastic_episode=dict(
returns=np.mean(eval_returns_stochastic),
lengths=int(np.mean(eval_lengths_stochastic))),
episode=dict(returns=np.mean(eval_returns_deterministic),
lengths=int(
np.mean(eval_lengths_deterministic))),
evaluation_error=dict(
stochastic_error=true_return -
np.mean(eval_returns_stochastic),
stochastic_abs=np.abs(
true_return - np.mean(eval_returns_stochastic)),
stochastic_rel=np.abs(true_return -
np.mean(eval_returns_stochastic))
/ true_return,
deterministic_error=true_return -
np.mean(eval_returns_deterministic),
deterministic_abs=np.abs(
true_return - np.mean(eval_returns_deterministic)),
deterministic_rel=np.abs(true_return - np.mean(
eval_returns_deterministic)) / true_return)))
# Generator
generator_dataset = None
for n_update in range(FLAGS.GAIL.g_iters):
data, ep_infos = virtual_runner.run(actor,
FLAGS.TRPO.rollout_samples,
stochastic=False)
# if FLAGS.TRPO.normalization:
# normalizers.state.update(data.state)
# normalizers.action.update(data.action)
# normalizers.diff.update(data.next_state - data.state)
if t == 0:
np.testing.assert_allclose(data.reward,
env.mb_step(data.state, data.action,
data.next_state)[0],
atol=1e-4,
rtol=1e-4)
if t == 0 and n_update == 0 and not FLAGS.GAIL.learn_absorbing:
data_ = data.copy()
data_ = data_.reshape(
[FLAGS.TRPO.rollout_samples // env.n_envs, env.n_envs])
for e in range(env.n_envs):
samples = data_[:, e]
masks = 1 - (samples.done | samples.timeout)[...,
np.newaxis]
masks = masks[:-1]
assert np.allclose(samples.state[1:] * masks,
samples.next_state[:-1] * masks)
t += FLAGS.TRPO.rollout_samples
data.reward = discriminator.get_reward(data.state, data.action,
data.next_state)
advantages, values = virtual_runner.compute_advantage(vfn, data)
train_info = algo.train(max_ent_coef, data, advantages, values)
fps = int(FLAGS.TRPO.rollout_samples / (time.time() - time_st))
train_info['reward'] = np.mean(data.reward)
train_info['fps'] = fps
expert_batch = expert_dataset.sample(256)
expert_state = np.stack([t.obs for t in expert_batch])
expert_action = np.stack([t.action for t in expert_batch])
expert_next_state = np.stack([t.next_obs for t in expert_batch])
train_mse_loss = policy.get_mse_loss(expert_state, expert_action,
expert_next_state)
eval_mse_loss = policy.get_mse_loss(eval_state, eval_action,
eval_next_state)
train_info['mse_loss'] = dict(train=train_mse_loss,
eval=eval_mse_loss)
log_kvs(prefix='TRPO', kvs=dict(iter=t, **train_info))
generator_dataset = data
# Discriminator
for n_update in range(FLAGS.GAIL.d_iters):
batch_size = FLAGS.GAIL.d_batch_size
d_train_infos = dict()
for generator_subset in generator_dataset.iterator(batch_size):
expert_batch = expert_dataset.sample(batch_size)
expert_state = np.stack([t.obs for t in expert_batch])
expert_action = np.stack([t.action for t in expert_batch])
expert_next_state = np.stack(
[t.next_obs for t in expert_batch])
expert_mask = None
train_info = discriminator.train(
expert_state,
expert_action,
expert_next_state,
generator_subset.state,
generator_subset.action,
generator_subset.next_state,
expert_mask,
)
for k, v in train_info.items():
if k not in d_train_infos:
d_train_infos[k] = []
d_train_infos[k].append(v)
d_train_infos = {k: np.mean(v) for k, v in d_train_infos.items()}
if n_update == FLAGS.GAIL.d_iters - 1:
log_kvs(prefix='Discriminator',
kvs=dict(iter=t, **d_train_infos))
if t % FLAGS.TRPO.save_freq == 0:
np.save('{}/stage-{}'.format(FLAGS.log_dir, t), saver.state_dict())
np.save('{}/final'.format(FLAGS.log_dir), saver.state_dict())
np.save('{}/final'.format(FLAGS.log_dir), saver.state_dict())
dict_result = dict()
for gamma in [0.9, 0.99, 0.999, 1.0]:
eval_returns, eval_lengths = evaluate_on_virtual_env(
actor, env_eval_stochastic, gamma=gamma)
dict_result[gamma] = [float(np.mean(eval_returns)), eval_returns]
logger.info('[%s]: %.4f', gamma, np.mean(eval_returns))
save_path = os.path.join(FLAGS.log_dir, 'evaluate.yml')
yaml.dump(dict_result, open(save_path, 'w'), default_flow_style=False)
def main():
FLAGS.set_seed()
FLAGS.freeze()
env = create_env(FLAGS.env.id, seed=FLAGS.seed, rescale_action=FLAGS.env.rescale_action)
dim_state = env.observation_space.shape[0]
dim_action = env.action_space.shape[0]
# expert actor
actor = Actor(dim_state, dim_action, init_std=0.)
subsampling_rate = env.max_episode_steps // FLAGS.GAIL.trajectory_size
expert_state, expert_action, expert_next_state, expert_reward = collect_samples_from_true_env(
env=env, actor=actor, nb_episode=FLAGS.GAIL.traj_limit, subsampling_rate=subsampling_rate)
logger.info('Collect % d samples avg return = %.4f', len(expert_state), np.mean(expert_reward))
eval_state, eval_action, eval_next_state, eval_reward = collect_samples_from_true_env(
env=env, actor=actor, nb_episode=3, seed=FLAGS.seed)
loc, scale = np.mean(expert_state, axis=0, keepdims=True), np.std(expert_state, axis=0, keepdims=True)
logger.info('loc = {}\nscale={}'.format(loc, scale))
normalizers = Normalizers(dim_action=dim_action, dim_state=dim_state)
policy = GaussianMLPPolicy(dim_state, dim_action, FLAGS.TRPO.policy_hidden_sizes,
output_diff=FLAGS.TRPO.output_diff, normalizers=normalizers)
bc_loss = BehavioralCloningLoss(dim_state, dim_action, policy, lr=float(FLAGS.BC.lr), train_std=FLAGS.BC.train_std)
tf.get_default_session().run(tf.global_variables_initializer())
set_random_seed(FLAGS.seed)
saver = nn.ModuleDict({'policy': policy, 'normalizers': normalizers})
print(saver)
# updater normalizer
normalizers.state.update(expert_state)
normalizers.action.update(expert_action)
normalizers.diff.update(expert_next_state - expert_state)
eval_gamma = 0.999
eval_returns, eval_lengths = evaluate_on_true_env(actor, env, gamma=eval_gamma)
logger.warning('Test policy true value = %.4f true length = %d (gamma = %f)',
np.mean(eval_returns), np.mean(eval_lengths), eval_gamma)
# virtual env
env_eval_stochastic = VirtualEnv(policy, env, n_envs=4, stochastic_model=True)
env_eval_deterministic = VirtualEnv(policy, env, n_envs=4, stochastic_model=False)
batch_size = FLAGS.BC.batch_size
true_return = np.mean(eval_returns)
for t in range(FLAGS.BC.max_iters):
if t % FLAGS.BC.eval_freq == 0:
eval_returns_stochastic, eval_lengths_stochastic = evaluate_on_virtual_env(
actor, env_eval_stochastic, gamma=eval_gamma)
eval_returns_deterministic, eval_lengths_deterministic = evaluate_on_virtual_env(
actor, env_eval_deterministic, gamma=eval_gamma)
log_kvs(prefix='Evaluate', kvs=dict(
iter=t, stochastic_episode=dict(
returns=np.mean(eval_returns_stochastic), lengths=int(np.mean(eval_lengths_stochastic))
), episode=dict(
returns=np.mean(eval_returns_deterministic), lengths=int(np.mean(eval_lengths_deterministic))
), evaluation_error=dict(
stochastic_error=true_return-np.mean(eval_returns_stochastic),
stochastic_abs=np.abs(true_return-np.mean(eval_returns_stochastic)),
stochastic_rel=np.abs(true_return-np.mean(eval_returns_stochastic))/true_return,
deterministic_error=true_return-np.mean(eval_returns_deterministic),
deterministic_abs=np.abs(true_return - np.mean(eval_returns_deterministic)),
deterministic_rel=np.abs(true_return-np.mean(eval_returns_deterministic))/true_return
)
))
indices = np.random.randint(low=0, high=len(expert_state), size=batch_size)
expert_state_ = expert_state[indices]
expert_action_ = expert_action[indices]
expert_next_state_ = expert_next_state[indices]
_, loss, grad_norm = bc_loss.get_loss(expert_state_, expert_action_, expert_next_state_,
fetch='train loss grad_norm')
if t % 100 == 0:
train_mse_loss = policy.get_mse_loss(expert_state_, expert_action_, expert_next_state_)
eval_mse_loss = policy.get_mse_loss(eval_state, eval_action, eval_next_state)
log_kvs(prefix='BC', kvs=dict(
iter=t, grad_norm=grad_norm, loss=loss, mse_loss=dict(train=train_mse_loss, eval=eval_mse_loss)
))
np.save('{}/final'.format(FLAGS.log_dir), saver.state_dict())
dict_result = dict()
for gamma in [0.9, 0.99, 0.999, 1.0]:
eval_returns, eval_lengths = evaluate_on_virtual_env(actor, env_eval_deterministic, gamma=gamma)
dict_result[gamma] = [float(np.mean(eval_returns)), eval_returns]
logger.info('[%s]: %.4f', gamma, np.mean(eval_returns))
save_path = os.path.join(FLAGS.log_dir, 'evaluate.yml')
yaml.dump(dict_result, open(save_path, 'w'), default_flow_style=False)
def main():
FLAGS.set_seed()
FLAGS.freeze()
env = create_env(FLAGS.env.id,
seed=FLAGS.seed,
rescale_action=FLAGS.env.rescale_action)
dim_state = env.observation_space.shape[0]
dim_action = env.action_space.shape[0]
normalizers = Normalizers(dim_action=dim_action, dim_state=dim_state)
policy = GaussianMLPPolicy(dim_state,
dim_action,
FLAGS.TRPO.policy_hidden_sizes,
output_diff=FLAGS.TRPO.output_diff,
normalizers=normalizers)
bc_loss = BehavioralCloningLoss(dim_state,
dim_action,
policy,
lr=float(FLAGS.BC.lr),
train_std=FLAGS.BC.train_std)
actor = Actor(dim_state, dim_action, FLAGS.SAC.actor_hidden_sizes)
tf.get_default_session().run(tf.global_variables_initializer())
subsampling_rate = env.max_episode_steps // FLAGS.GAIL.trajectory_size
# load expert dataset
set_random_seed(2020)
expert_dataset = load_expert_dataset(FLAGS.GAIL.buf_load)
expert_state = np.stack([t.obs for t in expert_dataset.buffer()])
expert_next_state = np.stack([t.next_obs for t in expert_dataset.buffer()])
expert_done = np.stack([t.done for t in expert_dataset.buffer()])
np.testing.assert_allclose(
expert_next_state[:-1] * (1 - expert_done[:-1][:, None]),
expert_state[1:] * (1 - expert_done[:-1][:, None]))
del expert_state, expert_next_state, expert_done
expert_reward = expert_dataset.get_average_reward()
logger.info('Expert Reward %f', expert_reward)
if FLAGS.GAIL.learn_absorbing:
expert_dataset.add_absorbing_states(env)
eval_batch = expert_dataset.sample(1024)
eval_state = np.stack([t.obs for t in eval_batch])
eval_action = np.stack([t.action for t in eval_batch])
eval_next_state = np.stack([t.next_obs for t in eval_batch])
logger.info('Sampled obs: %.4f, acs: %.4f', np.mean(eval_state),
np.mean(eval_action))
expert_dataset.subsample_trajectories(FLAGS.GAIL.traj_limit)
logger.info('Original dataset size {}'.format(len(expert_dataset)))
expert_dataset.subsample_transitions(subsampling_rate)
logger.info('Subsampled dataset size {}'.format(len(expert_dataset)))
logger.info('np random: %d random : %d', np.random.randint(1000),
random.randint(0, 1000))
set_random_seed(FLAGS.seed)
loader = nn.ModuleDict({'actor': actor})
loader.load_state_dict(
np.load(FLAGS.ckpt.policy_load, allow_pickle=True)[()])
logger.warning('Load expert policy from %s' % FLAGS.ckpt.policy_load)
saver = nn.ModuleDict({'policy': policy, 'normalizers': normalizers})
print(saver)
# updater normalizer
expert_state = np.stack([t.obs for t in expert_dataset.buffer()])
expert_action = np.stack([t.action for t in expert_dataset.buffer()])
expert_next_state = np.stack([t.next_obs for t in expert_dataset.buffer()])
normalizers.state.update(expert_state)
normalizers.action.update(expert_action)
normalizers.diff.update(expert_next_state - expert_state)
del expert_state, expert_action, expert_next_state
eval_gamma = 0.999
eval_returns, eval_lengths = evaluate_on_true_env(actor,
env,
gamma=eval_gamma)
logger.warning(
'Test policy true value = %.4f true length = %d (gamma = %f)',
np.mean(eval_returns), np.mean(eval_lengths), eval_gamma)
# virtual env
env_eval_stochastic = VirtualEnv(policy,
env,
n_envs=4,
stochastic_model=True)
env_eval_deterministic = VirtualEnv(policy,
env,
n_envs=4,
stochastic_model=False)
batch_size = FLAGS.BC.batch_size
true_return = np.mean(eval_returns)
for t in range(FLAGS.BC.max_iters):
if t % FLAGS.BC.eval_freq == 0:
eval_returns_stochastic, eval_lengths_stochastic = evaluate_on_virtual_env(
actor, env_eval_stochastic, gamma=eval_gamma)
eval_returns_deterministic, eval_lengths_deterministic = evaluate_on_virtual_env(
actor, env_eval_deterministic, gamma=eval_gamma)
log_kvs(
prefix='Evaluate',
kvs=dict(
iter=t,
stochastic_episode=dict(
returns=np.mean(eval_returns_stochastic),
lengths=int(np.mean(eval_lengths_stochastic))),
episode=dict(returns=np.mean(eval_returns_deterministic),
lengths=int(
np.mean(eval_lengths_deterministic))),
evaluation_error=dict(
stochastic_error=true_return -
np.mean(eval_returns_stochastic),
stochastic_abs=np.abs(
true_return - np.mean(eval_returns_stochastic)),
stochastic_rel=np.abs(true_return -
np.mean(eval_returns_stochastic))
/ true_return,
deterministic_error=true_return -
np.mean(eval_returns_deterministic),
deterministic_abs=np.abs(
true_return - np.mean(eval_returns_deterministic)),
deterministic_rel=np.abs(true_return - np.mean(
eval_returns_deterministic)) / true_return)))
expert_batch = expert_dataset.sample(batch_size)
expert_state = np.stack([t.obs for t in expert_batch])
expert_action = np.stack([t.action for t in expert_batch])
expert_next_state = np.stack([t.next_obs for t in expert_batch])
_, loss, grad_norm = bc_loss.get_loss(expert_state,
expert_action,
expert_next_state,
fetch='train loss grad_norm')
if t % 100 == 0:
train_mse_loss = policy.get_mse_loss(expert_state, expert_action,
expert_next_state)
eval_mse_loss = policy.get_mse_loss(eval_state, eval_action,
eval_next_state)
log_kvs(prefix='BC',
kvs=dict(iter=t,
grad_norm=grad_norm,
loss=loss,
mse_loss=dict(train=train_mse_loss,
eval=eval_mse_loss)))
np.save('{}/final'.format(FLAGS.log_dir), saver.state_dict())
dict_result = dict()
for gamma in [0.9, 0.99, 0.999, 1.0]:
eval_returns, eval_lengths = evaluate_on_virtual_env(
actor, env_eval_stochastic, gamma=gamma)
dict_result[gamma] = [float(np.mean(eval_returns)), eval_returns]
logger.info('[%s]: %.4f', gamma, np.mean(eval_returns))
save_path = os.path.join(FLAGS.log_dir, 'evaluate.yml')
yaml.dump(dict_result, open(save_path, 'w'), default_flow_style=False)
def main():
FLAGS.set_seed()
FLAGS.freeze()
env = create_env(FLAGS.env.id,
seed=FLAGS.seed,
rescale_action=FLAGS.env.rescale_action)
dim_state = env.observation_space.shape[0]
dim_action = env.action_space.shape[0]
bc_normalizers = Normalizers(dim_action=dim_action, dim_state=dim_state)
bc_policy = GaussianMLPPolicy(dim_state,
dim_action,
FLAGS.TRPO.policy_hidden_sizes,
output_diff=FLAGS.TRPO.output_diff,
normalizers=bc_normalizers)
gail_normalizers = Normalizers(dim_action=dim_action, dim_state=dim_state)
gail_policy = GaussianMLPPolicy(dim_state,
dim_action,
FLAGS.TRPO.policy_hidden_sizes,
output_diff=FLAGS.TRPO.output_diff,
normalizers=gail_normalizers)
actor = Actor(dim_state, dim_action, FLAGS.SAC.actor_hidden_sizes)
tf.get_default_session().run(tf.global_variables_initializer())
loader = nn.ModuleDict({'actor': actor})
policy_load = f'dataset/mb2/{FLAGS.env.id}/policy.npy'
loader.load_state_dict(np.load(policy_load, allow_pickle=True)[()])
logger.warning('Load expert policy from %s' % policy_load)
bc_policy_load = "benchmarks/mbrl_benchmark/mbrl2_bc_30_1000/mbrl2_bc-Walker2d-v2-100-2020-05-22-16-02-12/final.npy"
loader = nn.ModuleDict({
'policy': bc_policy,
'normalizers': bc_normalizers
})
loader.load_state_dict(np.load(bc_policy_load, allow_pickle=True)[()])
logger.warning('Load bc policy from %s' % bc_policy_load)
gail_policy_load = "benchmarks/mbrl_benchmark/mbrl2_gail_grad_penalty/mbrl2_gail-Walker2d-v2-100-2020-05-22-12-10-07/final.npy"
loader = nn.ModuleDict({
'policy': gail_policy,
'normalizers': gail_normalizers
})
loader.load_state_dict(np.load(gail_policy_load, allow_pickle=True)[()])
logger.warning('Load gail policy from %s' % gail_policy_load)
eval_gamma = 0.999
eval_returns, eval_lengths = evaluate_on_true_env(actor,
env,
gamma=eval_gamma)
logger.warning(
'Test policy true value = %.4f true length = %d (gamma = %f)',
np.mean(eval_returns), np.mean(eval_lengths), eval_gamma)
real_runner = Runner(env,
max_steps=env.max_episode_steps,
rescale_action=False)
# virtual env
env_bc_stochastic = VirtualEnv(bc_policy,
env,
n_envs=1,
stochastic_model=True)
env_bc_deterministic = VirtualEnv(bc_policy,
env,
n_envs=1,
stochastic_model=False)
runner_bc_stochastic = VirtualRunner(env_bc_stochastic,
max_steps=env.max_episode_steps,
rescale_action=False)
runner_bc_deterministic = VirtualRunner(env_bc_deterministic,
max_steps=env.max_episode_steps,
rescale_action=False)
env_gail_stochastic = VirtualEnv(gail_policy,
env,
n_envs=1,
stochastic_model=True)
env_gail_deterministic = VirtualEnv(gail_policy,
env,
n_envs=1,
stochastic_model=False)
runner_gail_stochastic = VirtualRunner(env_gail_stochastic,
max_steps=env.max_episode_steps)
runner_gail_deterministic = VirtualRunner(env_gail_deterministic,
max_steps=env.max_episode_steps)
data_actor, ep_infos = real_runner.run(actor,
n_samples=int(2e3),
stochastic=False)
returns = [info['return'] for info in ep_infos]
lengths = [info['length'] for info in ep_infos]
logger.info(
'Collect %d samples for actor avg return = %.4f avg length = %d',
len(data_actor), np.mean(returns), np.mean(lengths))
data_bc_stochastic, ep_infos = runner_bc_stochastic.run(actor,
n_samples=int(2e3),
stochastic=False)
returns = [info['return'] for info in ep_infos]
lengths = [info['length'] for info in ep_infos]
logger.info(
'Collect %d samples for bc stochastic policy avg return = %.4f avg length = %d',
len(data_bc_stochastic), np.mean(returns), np.mean(lengths))
reward_ref, _ = env.mb_step(data_bc_stochastic.state,
data_bc_stochastic.action,
data_bc_stochastic.next_state)
np.testing.assert_allclose(reward_ref,
data_bc_stochastic.reward,
rtol=1e-4,
atol=1e-4)
data_bc_deterministic, ep_infos = runner_bc_deterministic.run(
actor, n_samples=int(2e3), stochastic=False)
returns = [info['return'] for info in ep_infos]
lengths = [info['length'] for info in ep_infos]
logger.info(
'Collect %d samples for bc deterministic policy avg return = %.4f avg length = %d',
len(data_bc_deterministic), np.mean(returns), np.mean(lengths))
reward_ref, _ = env.mb_step(data_bc_deterministic.state,
data_bc_deterministic.action,
data_bc_deterministic.next_state)
np.testing.assert_allclose(reward_ref,
data_bc_deterministic.reward,
rtol=1e-4,
atol=1e-4)
data_gail_stochastic, ep_infos = runner_gail_stochastic.run(
actor, n_samples=int(2e3), stochastic=False)
returns = [info['return'] for info in ep_infos]
lengths = [info['length'] for info in ep_infos]
logger.info(
'Collect %d samples for gail stochastic policy avg return = %.4f avg length = %d',
len(data_gail_stochastic), np.mean(returns), np.mean(lengths))
data_gail_deterministic, ep_infos = runner_gail_deterministic.run(
actor, n_samples=int(2e3), stochastic=False)
returns = [info['return'] for info in ep_infos]
lengths = [info['length'] for info in ep_infos]
logger.info(
'Collect %d samples for gail deterministic policy avg return = %.4f avg length = %d',
len(data_bc_deterministic), np.mean(returns), np.mean(lengths))
t_sne = manifold.TSNE(init='pca', random_state=2020)
data = np.concatenate([
data.state for data in [
data_actor, data_bc_stochastic, data_bc_deterministic,
data_gail_stochastic, data_gail_deterministic
]
],
axis=0)
step = np.concatenate([
data.step for data in [
data_actor, data_bc_stochastic, data_bc_deterministic,
data_gail_stochastic, data_gail_deterministic
]
],
axis=0)
loc, scale = bc_normalizers.state.eval('mean std')
data = (data - loc) / (1e-6 + scale)
embedding = t_sne.fit_transform(data)
fig, axarrs = plt.subplots(nrows=1,
ncols=5,
figsize=[6 * 5, 4],
squeeze=False,
sharex=True,
sharey=True,
dpi=300)
start = 0
indices = 0
g2c = {}
for title in [
'expert', 'bc_stochastic', 'bc_deterministic', 'gail_stochastic',
'gail_deterministic'
]:
g2c[title] = axarrs[0][indices].scatter(embedding[start:start + 2000,
0],
embedding[start:start + 2000,
1],
c=step[start:start + 2000])
axarrs[0][indices].set_title(title)
indices += 1
start += 2000
plt.colorbar(list(g2c.values())[0], ax=axarrs.flatten())
plt.tight_layout()
plt.savefig(f'{FLAGS.log_dir}/visualize.png', bbox_inches='tight')
data = {
'expert': data_actor.state,
'bc_stochastic': data_bc_stochastic.state,
'bc_deterministic': data_bc_deterministic.state,
'gail_stochastic': data_gail_stochastic.state,
'gail_deterministic': data_gail_deterministic.state
}
np.savez(f'{FLAGS.log_dir}/data.npz', **data)