def train(sess, env_id, num_timesteps, timesteps_per_actor, autoencoders, seed):
from baselines.ppo1 import pposgd_novelty, mlp_policy_novelty, pposgd_novelty_projection, mlp_policy_mirror_novelty, \
pposgd_mirror_novelty, pposgd_mirror_novelty_projection
rank = MPI.COMM_WORLD.Get_rank()
workerseed = seed * 50 + 10000 * MPI.COMM_WORLD.Get_rank() if seed is not None else None
set_global_seeds(workerseed)
env = gym.make(env_id)
def policy_fn(name, ob_space, ac_space): # pylint: disable=W0613
return mlp_policy_mirror_novelty.MlpPolicyMirrorNovelty(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=64,
num_hid_layers=3,
mirror_loss=True,
observation_permutation=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
10,
15, 16, 17, 18,
11, 12, 13, 14,
23, 24, 25, 26,
19, 20, 21, 22],
action_permutation=[4, 5, 6, 7,
0, 1, 2, 3]
)
env.env.novel_autoencoders = autoencoders
env = bench.Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), str(rank)))
env.seed(seed + rank)
print("AUTOENCODER LENGTH: ", len(autoencoders))
model = pposgd_novelty.learn(env, policy_fn,
max_timesteps=num_timesteps,
# max_iters=30,
timesteps_per_actorbatch=timesteps_per_actor,
clip_param=0.2, entcoeff=0,
optim_epochs=3, optim_stepsize=1e-3, optim_batchsize=64,
gamma=0.99, lam=0.95,
schedule='linear',
callback=callback,
# Following params are kwargs
session=sess,
gap=5,
sym_loss_weight=1,
)
env.close()
return model
def train(sess, env_id, num_timesteps, timesteps_per_actor, autoencoders,
seed):
from baselines.ppo1 import pposgd_novelty, mlp_policy_novelty, pposgd_novelty_projection
rank = MPI.COMM_WORLD.Get_rank()
workerseed = seed * 50 + 10000 * MPI.COMM_WORLD.Get_rank(
) if seed is not None else None
set_global_seeds(workerseed)
env = gym.make(env_id)
def policy_fn(name, ob_space, ac_space): # pylint: disable=W0613
return mlp_policy_novelty.MlpPolicyNovelty(
name=name,
ob_space=ob_space,
ac_space=ac_space,
hid_size=64,
num_hid_layers=3,
)
env.env.novel_autoencoders = autoencoders
env = bench.Monitor(
env,
logger.get_dir() and osp.join(logger.get_dir(), str(rank)))
env.seed(seed + rank)
if len(autoencoders) == 0:
print("NO AUTOENCODER!!")
model = pposgd_novelty.learn(
env,
policy_fn,
max_timesteps=num_timesteps,
# max_iters=30,
timesteps_per_actorbatch=timesteps_per_actor,
clip_param=0.2,
entcoeff=0,
optim_epochs=3,
optim_stepsize=1e-3,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='linear',
callback=callback,
# Following params are kwargs
session=sess,
gap=5,
)
else:
print("AUTOENCODER LENGTH: ", len(autoencoders))
model = pposgd_novelty_projection.learn(
env,
policy_fn,
max_timesteps=num_timesteps,
# max_iters=30,
timesteps_per_actorbatch=timesteps_per_actor,
clip_param=0.2,
entcoeff=0,
optim_epochs=3,
optim_stepsize=1e-3,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='linear',
callback=callback,
# Following params are kwargs
session=sess,
gap=5,
)
env.close()
return model