def test_lstm_example():
import tensorflow as tf
from baselines.common import policies, models, cmd_util
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
# create vectorized environment
venv = DummyVecEnv([lambda: cmd_util.make_mujoco_env('Reacher-v2', seed=0)])
with tf.Session() as sess:
# build policy based on lstm network with 128 units
policy = policies.build_policy(venv, models.lstm(128))(nbatch=1, nsteps=1)
# initialize tensorflow variables
sess.run(tf.global_variables_initializer())
# prepare environment variables
ob = venv.reset()
state = policy.initial_state
done = [False]
step_counter = 0
# run a single episode until the end (i.e. until done)
while True:
action, _, state, _ = policy.step(ob, S=state, M=done)
ob, reward, done, _ = venv.step(action)
step_counter += 1
if done:
break
assert step_counter > 5
def test_lstm_example():
import tensorflow as tf
from baselines.common import policies, models, cmd_util
from baselines.common.vec_env.dummy_vec_env import DummyVecEnv
# create vectorized environment
venv = DummyVecEnv(
[lambda: cmd_util.make_mujoco_env('Reacher-v2', seed=0)])
with tf.Session() as sess:
# build policy based on lstm network with 128 units
policy = policies.build_policy(venv, models.lstm(128))(nbatch=1,
nsteps=1)
# initialize tensorflow variables
sess.run(tf.global_variables_initializer())
# prepare environment variables
ob = venv.reset()
state = policy.initial_state
done = [False]
step_counter = 0
# run a single episode until the end (i.e. until done)
while True:
action, _, state, _ = policy.step(ob, S=state, M=done)
ob, reward, done, _ = venv.step(action)
step_counter += 1
if done:
break
assert step_counter > 5
def main():
parser = argparse.ArgumentParser(description='Process procgen training arguments.')
parser.add_argument('--env_name', type=str, default='fruitbot', help='env to run on from procgen')
parser.add_argument('--num_envs', type=int, default=64, help='number of environments run simultaneously')
parser.add_argument('--distribution_mode', type=str, default='easy', choices=["easy", "hard", "exploration", "memory", "extreme"], help='level difficulty')
parser.add_argument('--num_levels', type=int, default=0, help='number of levels to train/test on')
parser.add_argument('--start_level', type=int, default=0, help='start level (used to avoid testing on seen levels)')
parser.add_argument('--num_timesteps', type=int, default=0, help='number of timesteps total to train/test on')
parser.add_argument('--save_frequency', type=int, default=0, help='checkpoint frequency')
parser.add_argument('--model_loc', type=str, default=None, help='location of pretrained model')
parser.add_argument('--results_loc', type=str, default=None, help='location of where to save current model/logs')
parser.add_argument('--eval', type=bool, default=False, help='if true, does not update model')
parser.add_argument('--data_aug', type=str, default='normal', help='whether to apply data augmentation')
parser.add_argument('--gray_p', type=float, default=0.8, help='p value for grayscale data augmentation')
parser.add_argument('--value_fn', type=str, default='fc', choices=['fc', 'gmm', 'lbmdp'], help='value function for ppo2 critic')
parser.add_argument('--cnn_fn', type=str, default='impala_cnn', choices=['impala_cnn', 'nature_cnn', 'impala_cnn_lstm', 'lstm'], help='cnn for featurization')
parser.add_argument('--entropy_fn', type=str, default='constant', choices=['constant', 'scaled'], help='function for entropy loss coefficient')
parser.add_argument('--ent_coef', type=float, default=0.01, help='coefficient applied to entropy loss')
parser.add_argument('--ent_scalar', type=float, default=1, help='coefficient applied within sigmoid to scaled entropy coefficient')
parser.add_argument('--seed', type=int, default=None, help='seed for tensorflow')
parser.add_argument('--gamma', type=float, default=0.999, help='discount factor')
parser.add_argument('--lam', type=float, default=0.95, help='advantage discount factor')
parser.add_argument('--lr', type=float, default=5e-4, help='learning rate for Adam')
parser.add_argument('--imp_h1', type=float, default=16, help='impala cnn first hidden state')
parser.add_argument('--imp_h2', type=float, default=64, help='impala cnn second hidden state')
parser.add_argument('--imp_h3', type=float, default=64, help='impala cnn third hidden state')
args = parser.parse_args()
logger.configure(dir=args.results_loc, format_strs=['csv', 'stdout'])
logger.info("Creating Environment")
venv = ProcgenEnv(num_envs=args.num_envs, env_name=args.env_name, num_levels=args.num_levels, start_level=args.start_level, distribution_mode=args.distribution_mode)
venv = VecExtractDictObs(venv, 'rgb')
venv = VecMonitor(
venv=venv,
filename=None,
keep_buf=100,
)
venv = VecNormalize(venv=venv, ob=False)
logger.info("Creating Tensorflow Session")
config = tf.ConfigProto()
sess = tf.Session(config=config)
sess.__enter__()
if args.cnn_fn == 'impala_cnn':
conv_fn = lambda x: build_impala_cnn(x, depths=[args.imp_h1,args.imp_h2,args.imp_h3], emb_size=256)
elif args.cnn_fn == 'nature_cnn':
conv_fn = lambda x: nature_cnn(x)
elif args.cnn_fn == 'impala_cnn_lstm':
conv_fn = impala_cnn_lstm()
elif args.cnn_fn == 'lstm':
conv_fn = lstm()
else:
conv_fn = mlp()
logger.info("Training")
learn(
network=conv_fn,
env=venv,
total_timesteps=args.num_timesteps,
eval_env = None,
seed=args.seed,
nsteps=256,
ent_coef=args.ent_coef,
lr=args.lr,
vf_coef=0.5,
max_grad_norm=0.5,
gamma=args.gamma,
lam=args.lam,
log_interval=args.save_frequency,
nminibatches=4,
noptepochs=3,
cliprange=0.2,
save_interval=0,
load_path=args.model_loc,
data_aug=args.data_aug,
args=args,
)