def learn(policy,
env,
expert,
seed,
total_timesteps=int(40e6),
gamma=0.99,
lam=0.95,
log_interval=1,
nprocs=32,
nsteps=20,
nstack=1,
ent_coef=0.01,
vf_coef=0.5,
vf_fisher_coef=1.0,
lr=0.05,
max_grad_norm=0.5,
kfac_clip=0.001,
save_interval=100,
lrschedule='linear'):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
make_model = lambda: Model(policy,
ob_space,
ac_space,
nenvs,
total_timesteps,
nprocs=nprocs,
nsteps=1024,
nstack=nstack,
ent_coef=ent_coef,
vf_coef=vf_coef,
vf_fisher_coef=vf_fisher_coef,
lr=lr,
max_grad_norm=max_grad_norm,
kfac_clip=kfac_clip,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
for _ in range(10000):
e_obs, e_actions, _, _ = expert.get_next_batch(1024)
e_a = [np.argmax(e_actions[k], axis=1) for k in range(len(e_actions))]
lld_loss = model.clone(e_obs, e_a)
print(lld_loss)
def create_env():
env = make_env.make_env(env_id)
env.seed(10)
# env = bench.Monitor(env, '/tmp/', allow_early_resets=True)
set_global_seeds(10)
return env
def create_env():
env = make_env.make_env('simple_spread')
env.seed(3)
env = bench.Monitor(env, '/tmp/', allow_early_resets=True)
set_global_seeds(3)
return env
lower_nb_actions = 2
TIME_STEPS = 10
TBD_total = 56
TBD_left = 38
TBD_straight = 56
TBD_right = 38
LSTM_HIDDEN = 128
ENCODE_LSTM_HIDDEN = 64
# Get the environment and extract the number of actions.
curr_path = os.path.dirname(__file__)
env = EndtoendEnv(setting_path=curr_path +
'/LasVSim/Scenario/Highway_endtoend/',
plan_horizon=30,
history_len=TIME_STEPS)
env = ObservationWrapper(env)
set_global_seeds(11) # set of seeds: [42, 0, 1, 2]
env.seed(11)
# nb_actions = env.action_space.n
def build_models():
# build upper model.
upper_model = Sequential(name='upper_model')
upper_model.add(layers.LSTM(
128,
input_shape=(TIME_STEPS,
TBD_total))) # A 3D tensor [batch, timesteps, inputdim]
upper_model.add(layers.Dense(upper_nb_actions, activation='relu'))
# build lower actor shared part----------------------------------------------
actor_lstm_model = Sequential(name='shared_actor_lstm_model')
def learn(policy,
env,
expert,
seed,
total_timesteps=int(40e6),
gamma=0.99,
lam=0.95,
log_interval=1,
nprocs=4,
nsteps=20,
nstack=1,
ent_coef=0.01,
vf_coef=0.5,
vf_fisher_coef=1.0,
lr=0.05,
max_grad_norm=0.5,
kfac_clip=0.001,
save_interval=1000,
lrschedule='linear',
batch_size=1024):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = env.num_envs
ob_space = env.observation_space
ac_space = env.action_space
make_model = lambda: Model(policy,
ob_space,
ac_space,
nenvs,
total_timesteps,
nprocs=nprocs,
nsteps=batch_size,
nstack=nstack,
ent_coef=ent_coef,
vf_coef=vf_coef,
vf_fisher_coef=vf_fisher_coef,
lr=lr,
max_grad_norm=max_grad_norm,
kfac_clip=kfac_clip,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
tstart = time.time()
coord = tf.train.Coordinator()
# enqueue_threads = [q_runner.create_threads(model.sess, coord=coord, start=True) for q_runner in model.q_runner]
print("-------------------------------")
print(total_timesteps // batch_size + 1)
print("-------------------------------")
for update in range(total_timesteps // batch_size + 1):
e_obs, e_actions, _, _ = expert.get_next_batch(batch_size)
e_a = [np.argmax(e_actions[k], axis=1) for k in range(len(e_actions))]
nseconds = time.time() - tstart
fps = int((update * batch_size) / nseconds)
lld_loss = model.clone(e_obs, e_a)[0]
# print(lld_loss)
if update % log_interval == 0 or update == 1:
logger.record_tabular("nupdates", update)
logger.record_tabular("total_timesteps", update * batch_size)
logger.record_tabular("fps", fps)
for k in range(model.num_agents):
logger.record_tabular("lld_loss %d" % k, float(lld_loss[k]))
logger.dump_tabular()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir():
savepath = osp.join(logger.get_dir(), 'checkpoint%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
coord.request_stop()