def train(env_id, num_timesteps, seed):
from baselines.ppo_pnp import mlp_policy, pposgd_simple, interactive_ppo, ppo_gail
U.make_session(num_cpu=1).__enter__()
def policy_fn(name, ob_space, ac_space):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
hid_size=64,
num_hid_layers=3)
env = JacoEnv(64, 64, 1, 1.0) #make_mujoco_env(env_id, seed)
dataset = Mujoco_Dset(expert_path='data/pnp_demo.npz', traj_limitation=-1)
reward_giver = TransitionClassifier(env, 100, entcoeff=1e-3)
ppo_gail.learn(
env,
policy_fn,
reward_giver,
dataset,
max_timesteps=num_timesteps,
timesteps_per_actorbatch=2048,
clip_param=0.2,
entcoeff=0.0,
optim_epochs=10,
optim_stepsize=3e-4,
optim_batchsize=64,
gamma=0.99,
lam=0.95,
schedule='linear',
)
env.close()
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2)
env = bench.Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
dataset = Mujoco_Dset(expert_path=args.expert_path, traj_limitation=args.traj_limitation)
savedir_fname = learn(env,
policy_fn,
dataset,
max_iters=args.BC_max_iter,
ckpt_dir=args.checkpoint_dir,
log_dir=args.log_dir,
task_name=task_name,
verbose=True)
avg_len, avg_ret = runner(env,
policy_fn,
savedir_fname,
timesteps_per_batch=1024,
number_trajs=10,
stochastic_policy=args.stochastic_policy,
save=args.save_sample,
reuse=True)
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
reuse=reuse,
hid_size=args.policy_hidden_size,
num_hid_layers=2,
gaussian_fixed_var=False,
obs_normalize=True)
env = bench.Monitor(
env,
logger.get_dir() and osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
logger.configure(
os.path.join("log", "BC", args.env_id,
"subsample_{}".format(args.subsample_freq),
"traj_{}".format(args.traj_limitation)))
args.expert_path = 'dataset/{}.npz'.format(args.env_id).lower().replace(
"-v1", "") # set expert path
dataset = Mujoco_Dset(expert_path=args.expert_path,
traj_limitation=args.traj_limitation,
data_subsample_freq=args.subsample_freq)
learn(env, policy_fn, dataset, max_iters=args.BC_max_iter, verbose=True)
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
reuse=reuse,
hid_size=args.policy_hidden_size,
num_hid_layers=2)
env = bench.Monitor(
env,
logger.get_dir() and osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
if args.task == 'train':
dataset = Mujoco_Dset(expert_path=args.expert_path,
traj_limitation=args.traj_limitation)
reward_giver = TransitionClassifier(env,
args.adversary_hidden_size,
entcoeff=args.adversary_entcoeff)
train(
env,
args.seed,
policy_fn,
reward_giver,
dataset,
args.algo,
args.g_step,
args.d_step,
args.policy_entcoeff,
args.num_timesteps,
args.save_per_iter,
args.checkpoint_dir,
args.log_dir,
args.pretrained, #false
args.BC_max_iter,
task_name)
elif args.task == 'evaluate':
runner(env,
policy_fn,
args.load_model_path,
timesteps_per_batch=1024,
number_trajs=10,
stochastic_policy=args.stochastic_policy,
save=args.save_sample)
else:
raise NotImplementedError
env.close()
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = robosuite.make(args.env_id,
ignore_done=True,
use_camera_obs=False,
has_renderer=True,
control_freq=100,
gripper_visualization=True,
reward_shaping=True,
#box_pos = [0.63522776, -0.3287869, 0.82162434], # shift2
#box_quat=[0.6775825618903728, 0, 0, 0.679425538604203], # shift2
#box_pos = [0.23522776, 0.2287869, 0.82162434], #shift3
#box_quat=[0.3775825618903728, 0, 0, 0.679425538604203], #shift3
#box_pos = [0.53522776, 0.3287869, 0.82162434], #shift4
#box_quat=[0.5775825618903728, 0, 0, 0.679425538604203], #shift4
#box_pos = [0.53522776, 0.1287869, 0.82162434], #shift5
#box_quat=[0.4775825618903728, 0, 0, 0.679425538604203], #shift5
#box_pos = [0.48522776, -0.187869, 0.82162434], #shift6
#box_quat=[0.8775825618903728, 0, 0, 0.679425538604203], #shift6
box_pos = [0.43522776, -0.367869, 0.82162434], #shift7
box_quat=[0.2775825618903728, 0, 0, 0.679425538604203], #shift7
) # Switch from gym to robosuite, also add reward shaping to see reach goal
env = GymWrapper(env) # wrap in the gym environment
# Environment joints should be clipped at 1 and -1 for sawyer
# Task
#task = 'train'
task = 'evaluate'
# parser.add_argument('--task', type=str, choices=['train', 'evaluate', 'sample'], default='train')
# Expert Path
#expert_path = '/home/mastercljohnson/Robotics/GAIL_Part/mod_surreal/robosuite/models/assets/demonstrations/ac100/combined/combined_0.npz' # path for 100 trajectories
expert_path = '/home/mastercljohnson/Robotics/GAIL_Part/mod_surreal/robosuite/models/assets/demonstrations/120_shift7/combined/combined_0.npz' # path for 100 trajectories
#parser.add_argument('--expert_path', type=str, default='data/deterministic.trpo.Hopper.0.00.npz')
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy_sawyer.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2)
env = bench.Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), "monitor.json"), allow_early_resets=True)
#env.seed(args.seed) # Sawyer does not have seed
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
#if not os.path.isdir(args.log_dir):
# os.makedirs(args.log_dir)
logger.log("log_directories: ",args.log_dir)
logger.log("environment action space range: ", env.action_space) #logging the action space
if task == 'train':
dataset = Mujoco_Dset(expert_path=expert_path, traj_limitation=args.traj_limitation)
# Check dimensions of the dataset
#print("dimension of inputs", dataset.dset.inputs.shape) # dims seem correct
#print("dimension of inputs", dataset.dset.labels.shape) # dims seem correct
reward_giver = TransitionClassifier(env, args.adversary_hidden_size, entcoeff=args.adversary_entcoeff)
train(env,
args.seed,
policy_fn,
reward_giver,
dataset,
args.algo,
args.g_step,
args.d_step,
args.policy_entcoeff,
args.num_timesteps,
args.save_per_iter,
args.checkpoint_dir,
args.log_dir,
args.pretrained,
args.BC_max_iter,
task_name
)
elif task == 'evaluate':
# Create the playback environment
play_env = robosuite.make(args.env_id,
ignore_done=True,
use_camera_obs=False,
has_renderer=True,
control_freq=100,
gripper_visualization=True,
#box_pos = [0.63522776, -0.3287869, 0.82162434], # shift2
#box_quat=[0.6775825618903728, 0, 0, 0.679425538604203], # shift2
#box_pos = [0.23522776, 0.2287869, 0.82162434], #shift3
#box_quat=[0.3775825618903728, 0, 0, 0.679425538604203], #shift3
#box_pos = [0.53522776, 0.3287869, 0.82162434], #shift4
#box_quat=[0.5775825618903728, 0, 0, 0.679425538604203], #shift4
#box_pos = [0.53522776, 0.1287869, 0.82162434], #shift5
#box_quat=[0.4775825618903728, 0, 0, 0.679425538604203], #shift5
#box_pos = [0.48522776, -0.187869, 0.82162434], #shift6
#box_quat=[0.8775825618903728, 0, 0, 0.679425538604203], #shift6
box_pos = [0.43522776, -0.367869, 0.82162434], #shift7
box_quat=[0.2775825618903728, 0, 0, 0.679425538604203], #shift7
)
#play_env.viewer.set_camera(camera_id=2) # Switch views for eval
runner(env,
play_env,
policy_fn,
args.load_model_path,
timesteps_per_batch=4000, # Change time step per batch to be more reasonable
number_trajs=20, # change from 10 to 1 for evaluation
stochastic_policy=args.stochastic_policy,
save=args.save_sample
)
else:
raise NotImplementedError
env.close()
def learn(
*,
network,
env,
eval_env,
timesteps_per_batch=1000, # what to train on
max_kl=0.001,
cg_iters=10,
gamma=0.99,
lam=1.0, # advantage estimation
seed=None,
ent_coef=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
vf_iters=3,
num_epochs=1000,
callback=None,
load_path=None,
log_dir=None,
env_id=None,
evaluation_freq=10,
pretrain=False,
expert_path=None,
BC_max_iter=1e4,
**network_kwargs):
'''
learn a policy function with TRPO algorithm
Parameters:
----------
network neural network to learn. Can be either string ('mlp', 'cnn', 'lstm', 'lnlstm' for basic types)
or function that takes input placeholder and returns tuple (output, None) for feedforward nets
or (output, (state_placeholder, state_output, mask_placeholder)) for recurrent nets
env environment (one of the gym environments or wrapped via baselines.common.vec_env.VecEnv-type class
timesteps_per_batch timesteps per gradient estimation batch
max_kl max KL divergence between old policy and new policy ( KL(pi_old || pi) )
ent_coef coefficient of policy entropy term in the optimization objective
cg_iters number of iterations of conjugate gradient algorithm
cg_damping conjugate gradient damping
vf_stepsize learning rate for adam optimizer used to optimie value function loss
vf_iters number of iterations of value function optimization iterations per each policy optimization step
total_timesteps max number of timesteps
max_episodes max number of episodes
max_iters maximum number of policy optimization iterations
callback function to be called with (locals(), globals()) each policy optimization step
load_path str, path to load the model from (default: None, i.e. no model is loaded)
**network_kwargs keyword arguments to the policy / network builder. See baselines.common/policies.py/build_policy and arguments to a particular type of network
Returns:
-------
learnt model
'''
# Configure log.
log_dir = os.path.join("log", "trpo", env_id,
"pretrained_" + str(pretrain), str(seed))
logger.configure(dir=log_dir)
if MPI is not None:
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
else:
nworkers = 1
rank = 0
cpus_per_worker = 1
U.get_session(config=tf.ConfigProto(
allow_soft_placement=True,
inter_op_parallelism_threads=cpus_per_worker,
intra_op_parallelism_threads=cpus_per_worker,
))
policy = build_policy(env, network, value_network='copy', **network_kwargs)
set_global_seeds(seed)
# Pretrain.
mujo_dataset = Mujoco_Dset(expert_path=expert_path)
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
ob = observation_placeholder(ob_space)
pretrained_weight = None
if pretrain and (BC_max_iter > 0):
# Pretrain with behavior cloning
from baselines.trpo_mpi import behavior_clone
pretrained_weight, pi = behavior_clone.learn(ob,
policy,
mujo_dataset,
max_iters=BC_max_iter)
evaluate_policy(pi, eval_env, -2, timesteps_per_batch, 0)
else:
with tf.variable_scope("pi"):
pi = policy(observ_placeholder=ob)
with tf.variable_scope("oldpi"):
oldpi = policy(observ_placeholder=ob)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = tf.reduce_mean(kloldnew)
meanent = tf.reduce_mean(ent)
entbonus = ent_coef * meanent
vferr = tf.reduce_mean(tf.square(pi.vf - ret))
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = tf.reduce_mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = get_trainable_variables("pi")
# var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("pol")]
# vf_var_list = [v for v in all_var_list if v.name.split("/")[1].startswith("vf")]
var_list = get_pi_trainable_variables("pi")
vf_var_list = get_vf_trainable_variables("pi")
vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(var_list)
klgrads = tf.gradients(dist, var_list)
flat_tangent = tf.placeholder(dtype=tf.float32,
shape=[None],
name="flat_tan")
shapes = [var.get_shape().as_list() for var in var_list]
start = 0
tangents = []
for shape in shapes:
sz = U.intprod(shape)
tangents.append(tf.reshape(flat_tangent[start:start + sz], shape))
start += sz
gvp = tf.add_n([
tf.reduce_sum(g * tangent)
for (g, tangent) in zipsame(klgrads, tangents)
]) #pylint: disable=E1111
fvp = U.flatgrad(gvp, var_list)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(get_variables("oldpi"), get_variables("pi"))
])
compute_losses = U.function([ob, ac, atarg], losses)
compute_lossandgrad = U.function([ob, ac, atarg], losses +
[U.flatgrad(optimgain, var_list)])
compute_fvp = U.function([flat_tangent, ob, ac, atarg], fvp)
compute_vflossandgrad = U.function([ob, ret],
U.flatgrad(vferr, vf_var_list))
@contextmanager
def timed(msg):
if rank == 0:
print(colorize(msg, color='magenta'))
tstart = time.time()
yield
print(
colorize("done in %.3f seconds" % (time.time() - tstart),
color='magenta'))
else:
yield
def allmean(x):
assert isinstance(x, np.ndarray)
if MPI is not None:
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
else:
out = np.copy(x)
return out
U.initialize()
if load_path is not None:
pi.load(load_path)
th_init = get_flat()
if MPI is not None:
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# if provide pretrained weight
if pretrained_weight is not None:
U.load_variables(pretrained_weight,
variables=tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, "pi"))
evaluate_policy(pi, eval_env, -1, timesteps_per_batch, 0)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_batch,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=40) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=40) # rolling buffer for episode rewards
for epoch in range(num_epochs):
if callback: callback(locals(), globals())
logger.log("********** Epoch %i ************" % epoch)
with timed("sampling"):
seg = seg_gen.__next__()
add_vtarg_and_adv(seg, gamma, lam)
# ob, ac, atarg, ret, td1ret = map(np.concatenate, (obs, acs, atargs, rets, td1rets))
ob, ac, atarg, tdlamret = seg["ob"], seg["ac"], seg["adv"], seg[
"tdlamret"]
vpredbefore = seg["vpred"] # predicted value function before udpate
atarg = (atarg - atarg.mean()
) / atarg.std() # standardized advantage function estimate
# if hasattr(pi, "ret_rms"): pi.ret_rms.update(tdlamret)
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
assign_old_eq_new() # set old parameter values to new parameter values
with timed("computegrad"):
*lossbefore, g = compute_lossandgrad(*args)
lossbefore = allmean(np.array(lossbefore))
g = allmean(g)
if np.allclose(g, 0):
logger.log("Got zero gradient. not updating")
else:
with timed("cg"):
stepdir = cg(fisher_vector_product,
g,
cg_iters=cg_iters,
verbose=rank == 0)
assert np.isfinite(stepdir).all()
shs = .5 * stepdir.dot(fisher_vector_product(stepdir))
lm = np.sqrt(shs / max_kl)
# logger.log("lagrange multiplier:", lm, "gnorm:", np.linalg.norm(g))
fullstep = stepdir / lm
expectedimprove = g.dot(fullstep)
surrbefore = lossbefore[0]
stepsize = 1.0
thbefore = get_flat()
for _ in range(10):
thnew = thbefore + fullstep * stepsize
set_from_flat(thnew)
meanlosses = surr, kl, *_ = allmean(
np.array(compute_losses(*args)))
improve = surr - surrbefore
logger.log("Expected: %.3f Actual: %.3f" %
(expectedimprove, improve))
if not np.isfinite(meanlosses).all():
logger.log("Got non-finite value of losses -- bad!")
elif kl > max_kl * 1.5:
logger.log("violated KL constraint. shrinking step.")
elif improve < 0:
logger.log("surrogate didn't improve. shrinking step.")
else:
logger.log("Stepsize OK!")
break
stepsize *= .5
else:
logger.log("couldn't compute a good step")
set_from_flat(thbefore)
if nworkers > 1 and iters_so_far % 20 == 0:
paramsums = MPI.COMM_WORLD.allgather(
(thnew.sum(), vfadam.getflat().sum())) # list of tuples
assert all(
np.allclose(ps, paramsums[0]) for ps in paramsums[1:])
with timed("vf"):
for _ in range(vf_iters):
for (mbob, mbret) in dataset.iterbatches(
(seg["ob"], seg["tdlamret"]),
include_final_partial_batch=False,
batch_size=64):
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
if epoch % evaluation_freq == 0:
evaluate_policy(pi, eval_env, epoch, timesteps_per_batch, tstart)
return pi
def load_dataset(expert_path):
dataset = Mujoco_Dset(expert_path=expert_path)
return dataset
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
import MujocoManip as MM
if args.task == 'train':
env_name, user_name = osp.basename(
args.expert_path).split('.')[0].split('_')
else:
env_name, user_name = osp.basename(args.load_model_path).split('.')[:2]
wrapper = '%sWrapper' % env_name
render = True if args.task == 'evaluate' else False
if env_name == 'SawyerLiftEnv':
env = MM.make(wrapper,
ignore_done=False,
use_eef_ctrl=False,
gripper_visualization=True,
use_camera_obs=False,
has_renderer=render,
reward_shaping=True,
has_offscreen_renderer=render)
elif env_name == 'SawyerBinsEnv':
env = MM.make(
wrapper,
ignore_done=False,
use_eef_ctrl=False,
gripper_visualization=True,
use_camera_obs=False,
has_renderer=render,
reward_shaping=True,
single_object_mode=False if 'hard' in user_name.lower() else True,
has_offscreen_renderer=render)
elif env_name == 'SawyerPegsEnv':
env = MM.make(
wrapper,
ignore_done=False,
use_eef_ctrl=False,
gripper_visualization=True,
use_camera_obs=False,
has_renderer=render,
reward_shaping=True,
single_object_mode=False if 'hard' in user_name.lower() else True,
has_offscreen_renderer=render)
else:
raise NotImplementedError
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
reuse=reuse,
hid_size=args.policy_hidden_size,
num_hid_layers=2)
env = bench.Monitor(
env,
logger.get_dir() and osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(env_name, user_name) + '_%s_%s' % (
args.algo, 1 if not args.mix_reward else args.rew_lambda)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
os.makedirs(args.log_dir, exist_ok=True)
if args.task == 'train':
dataset = Mujoco_Dset(expert_path=args.expert_path,
traj_limitation=args.traj_limitation)
reward_giver = TransitionClassifier(env,
args.adversary_hidden_size,
entcoeff=args.adversary_entcoeff)
train(env, args.seed, policy_fn, reward_giver, dataset, args.algo,
args.g_step, args.d_step, args.policy_entcoeff,
args.num_timesteps, args.save_per_iter, args.checkpoint_dir,
args.log_dir, args.pretrained, args.BC_max_iter, args.rew_lambda,
args.mix_reward, task_name, args.frame_stack)
elif args.task == 'evaluate':
visualizer(env,
policy_fn,
args.load_model_path,
timesteps_per_batch=env.env.horizon,
number_trajs=10,
stochastic_policy=args.stochastic_policy,
save=args.save_sample)
else:
raise NotImplementedError
env.close()
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = robosuite.make(
args.env_id,
ignore_done=True,
use_camera_obs=False,
has_renderer=True,
control_freq=100,
gripper_visualization=True,
reward_shaping=True,
#box_pos = [0.63522776, -0.3287869, 0.82162434], # shift2
#box_quat=[0.6775825618903728, 0, 0, 0.679425538604203], # shift2
) # Switch from gym to robosuite, also add reward shaping to see reach goal
env = GymWrapper(env) # wrap in the gym environment
#task = 'train'
task = 'evaluate'
# Expert Path
expert_path = '/home/mastercljohnson/Robotics/GAIL_Part/mod_surreal/robosuite/models/assets/demonstrations/150_grasp_shift2/combined/combined_0.npz' # path for 100 trajectories
#parser.add_argument('--expert_path', type=str, default='data/deterministic.trpo.Hopper.0.00.npz')
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy_sawyer.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
reuse=reuse,
hid_size=args.policy_hidden_size,
num_hid_layers=2)
env = bench.Monitor(env,
logger.get_dir()
and osp.join(logger.get_dir(), "monitor.json"),
allow_early_resets=True)
# Note: taking away the bench monitor wrapping allows rendering
#env.seed(args.seed) # Sawyer does not have seed
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
logger.log("log_directories: ", args.log_dir)
logger.log("environment action space range: ",
env.action_space) #logging the action space
#------- Run policy for reaching ---------#
play_env = robosuite.make(
args.env_id,
ignore_done=True,
use_camera_obs=False,
has_renderer=True,
control_freq=100,
gripper_visualization=True,
#box_pos = [0.63522776, -0.3287869, 0.82162434], # shift2
#box_quat=[0.6775825618903728, 0, 0, 0.679425538604203], # shift2
)
play_env = GymWrapper(play_env)
#Weights are loaded from reach model grasp_strange
#play_env.viewer.set_camera(camera_id=2) # Switch views for eval
# Setup network
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi_reach = policy_fn("pi", ob_space, ac_space, reuse=False)
# Hack for loading policies using tensorflow
init_op = tf.compat.v1.global_variables_initializer()
saver = tf.compat.v1.train.Saver(max_to_keep=5)
with tf.compat.v1.Session() as sess:
sess.run(init_op)
# Load Checkpoint
ckpt_path = './reach_and_grasp_weights/reach_one/trpo_gail.transition_limitation_2100.SawyerLift.g_step_1.d_step_1.policy_entcoeff_0.adversary_entcoeff_0.001.seed_0/'
ckpt = tf.compat.v1.train.get_checkpoint_state(ckpt_path)
saver.restore(sess, ckpt.model_checkpoint_path)
# Create the playback environment
_, _, last_ob, last_jpos = runner_1_traj(
play_env,
pi_reach,
None,
timesteps_per_batch=3500,
number_trajs=1,
stochastic_policy=args.stochastic_policy,
save=False)
if task == 'train':
play_env.close()
dataset = Mujoco_Dset(expert_path=expert_path,
traj_limitation=args.traj_limitation)
reward_giver = TransitionClassifier(env,
args.adversary_hidden_size,
entcoeff=args.adversary_entcoeff)
train_grasp(env, last_ob, last_jpos, args.seed, policy_fn,
reward_giver, dataset, args.algo, args.g_step, args.d_step,
args.policy_entcoeff, args.num_timesteps,
args.save_per_iter, args.checkpoint_dir, args.log_dir,
args.pretrained, args.BC_max_iter, task_name)
elif task == 'evaluate':
pi_grasp = policy_fn("pi_grasp", ob_space, ac_space, reuse=False)
saver_2 = tf.compat.v1.train.Saver(max_to_keep=5)
with tf.compat.v1.Session() as sess:
sess.run(init_op)
ckpt_path_2 = './reach_and_grasp_weights/grasp_shift1_after_reach/grasptrpo_gail.transition_limitation_2000.SawyerLift.g_step_1.d_step_1.policy_entcoeff_0.adversary_entcoeff_0.001.seed_0/'
ckpt_2 = tf.compat.v1.train.get_checkpoint_state(ckpt_path_2)
saver_2.restore(sess, ckpt_2.model_checkpoint_path)
tt = 0
ob = last_ob
while True:
ac, vpred = pi_grasp.act(False, ob)
ob, rew, new, _ = play_env.step(ac)
play_env.render() # check the running in for the first part
#logger.log("rendering for reach policy")
if new or tt >= args.traj_limitation:
break
tt += 1
play_env.close()
env.close()
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
import MujocoManip as MM
if args.task == 'train':
env_name, user_name = osp.basename(
args.expert_path).split('.')[0].split('_')
else:
uenv, user_name = osp.basename(args.load_model_path).split('.')[:2]
env_name = uenv.split('_')[1]
wrapper = '%sWrapper' % env_name
render = True if args.task == 'evaluate' else False
print('%s initialized.' % wrapper)
bin_dict = dict(milk=0, bread=1, cereal=2, can=3)
peg_dict = dict(square=0, round=1)
if env_name == 'SawyerLiftEnv':
env = MM.make(wrapper,
ignore_done=False,
use_eef_ctrl=False,
gripper_visualization=True,
use_camera_obs=False,
has_renderer=render,
reward_shaping=True,
has_offscreen_renderer=False)
elif env_name == 'SawyerBinsEnv':
env = MM.make(
wrapper,
ignore_done=False,
use_eef_ctrl=False,
gripper_visualization=True,
use_camera_obs=False,
has_renderer=render,
reward_shaping=True,
single_object_mode=False if 'hard' in user_name.lower() else True,
has_offscreen_renderer=False,
selected_bin=None
if 'hard' in user_name.lower() else bin_dict[user_name.lower()])
elif env_name == 'SawyerPegsEnv':
env = MM.make(wrapper,
ignore_done=False,
use_eef_ctrl=False,
gripper_visualization=True,
use_camera_obs=False,
has_renderer=render,
reward_shaping=True,
single_object_mode=False if user_name.lower() else True,
has_offscreen_renderer=False,
selected_bin=None if 'hard' in user_name.lower() else
peg_dict[user_name.lower()])
else:
raise NotImplementedError
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
reuse=reuse,
hid_size=args.policy_hidden_size,
num_hid_layers=3)
env = bench.Monitor(
env,
logger.get_dir() and osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(env_name, user_name)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
if args.task == 'train':
dataset = Mujoco_Dset(expert_path=args.expert_path,
traj_limitation=args.traj_limitation)
savedir_fname = learn(env,
policy_fn,
dataset,
max_iters=args.BC_max_iter,
ckpt_dir=args.checkpoint_dir,
log_dir=args.log_dir,
task_name=task_name,
verbose=True)
elif args.task == 'evaluate':
visualizer(env,
policy_fn,
args.load_model_path,
env.env.horizon,
10,
args.stochastic_policy,
save=args.save_sample)
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
logger.configure()
env = bench.Monitor(env, logger.get_dir() and
osp.join(logger.get_dir(), "monitor.json"))
env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, task_name)
if args.task == 'train':
from baselines.gail import mlp_policy
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2)
dataset = Mujoco_Dset(expert_path=args.expert_path, traj_limitation=args.traj_limitation)
if args.states_only:
reward_giver = WeakTransitionClassifier(env, args.adversary_hidden_size, entcoeff=args.adversary_entcoeff)
else:
reward_giver = TransitionClassifier(env, args.adversary_hidden_size, entcoeff=args.adversary_entcoeff)
train(env,
args.seed,
policy_fn,
reward_giver,
dataset,
args.algo,
args.g_step,
args.d_step,
args.policy_entcoeff,
args.num_timesteps,
args.save_per_iter,
args.checkpoint_dir,
args.log_dir,
args.pretrained,
args.BC_max_iter,
task_name,
args.states_only
)
elif args.task == 'evaluate':
from baselines.gail import mlp_policy
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
reuse=reuse, hid_size=args.policy_hidden_size, num_hid_layers=2)
runner(env,
policy_fn,
args.load_model_path,
timesteps_per_batch=1024,
number_trajs=args.traj_limitation,
stochastic_policy=args.stochastic_policy,
save=args.save_sample
)
elif args.task == 'expert_train':
from baselines.trpo_mpi import trpo_mpi as original_trpo
from baselines.ppo1.mlp_policy import MlpPolicy as OriginalMlpPolicy
def policy_fn(name, ob_space, ac_space, reuse=False):
return OriginalMlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=args.policy_hidden_size, num_hid_layers=2)
original_trpo.learn(env, policy_fn, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, cg_damping=0.1,
max_timesteps=args.num_timesteps, gamma=0.99, lam=0.98, vf_iters=5, vf_stepsize=1e-3)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), args.save_model_path)
elif args.task == 'expert_gen':
from baselines.trpo_mpi import trpo_mpi as original_trpo
from baselines.ppo1.mlp_policy import MlpPolicy as OriginalMlpPolicy
def policy_fn(name, ob_space, ac_space, reuse=False):
return OriginalMlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=args.policy_hidden_size, num_hid_layers=2)
runner(env,
policy_fn,
args.save_model_path,
timesteps_per_batch=1024,
number_trajs=args.traj_limitation,
stochastic_policy=args.stochastic_policy,
save=args.save_sample
)
else:
raise NotImplementedError
env.close()
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
# env = DelayRewardWrapper(env, args.delay_freq, args.max_path_length)
eval_env = gym.make(args.env_id)
logger.configure(
os.path.join("log", "GAIL", args.env_id,
"subsample_{}".format(args.subsample_freq),
"traj_{}".format(args.traj_limitation),
"seed_{}".format(args.seed)))
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
reuse=reuse,
hid_size=args.policy_hidden_size,
num_hid_layers=2,
gaussian_fixed_var=args.gaussian_fixed_var,
obs_normalize=args.obs_normalize)
env.seed(args.seed)
eval_env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir, "GAIL", task_name)
if args.task == 'train':
dataset = Mujoco_Dset(expert_path=args.expert_path,
traj_limitation=args.traj_limitation,
data_subsample_freq=args.subsample_freq)
reward_giver = TransitionClassifier(env,
args.adversary_hidden_size,
entcoeff=args.adversary_entcoeff,
obs_normalize=args.obs_normalize)
train(
env,
eval_env,
args.seed,
policy_fn,
reward_giver,
dataset,
args.algo,
args.g_step,
args.d_step,
args.policy_entcoeff,
args.save_per_iter,
args.checkpoint_dir,
args.log_dir,
args.pretrained,
args.BC_max_iter,
args.num_epochs,
args.evaluation_freq,
args.timesteps_per_batch,
task_name,
)
elif args.task == 'evaluate':
runner(env,
policy_fn,
args.load_model_path,
timesteps_per_batch=args.timesteps_per_batch,
number_trajs=10,
stochastic_policy=args.stochastic_policy,
save=args.save_sample)
else:
raise NotImplementedError
env.close()
def setup_and_learn(env,
nb_epochs,
nb_epoch_cycles,
render_eval,
reward_scale,
render,
actor,
critic,
classifier,
normalize_returns,
normalize_observations,
critic_l2_reg,
classifier_l2_reg,
actor_lr,
critic_lr,
classifier_lr,
action_noise,
popart,
gamma,
clip_norm,
nb_train_steps,
nb_rollout_steps,
nb_eval_steps,
batch_size,
memory,
fifomemory,
tau=0.01,
eval_env=None,
callback=None,
entropy_coeff=1.,
reward_giver=None,
expert_dataset=None,
g_step=4,
d_step=1,
d_stepsize=3e-4,
max_timesteps=0,
max_iters=0,
timesteps_per_batch=1024,
adversary_hidden_size=100,
adversary_entcoeff=1e-3,
task='train',
expert_path=None): # TODO: max_episodes
"""
set up learning agent and execute training
"""
logger.info('Initialize policy')
logger.info('noisynet implementation of DDPG')
assert task == 'train'
assert (np.abs(env.action_space.low) == env.action_space.high
).all() # we assume symmetric actions.
max_action = env.action_space.high
logger.info(
'scaling actions by {} before executing in env'.format(max_action))
agent = DDPG_paramnoise(actor,
critic,
classifier,
memory,
fifomemory,
env.observation_space.shape,
env.action_space.shape,
gamma=gamma,
tau=tau,
normalize_returns=normalize_returns,
normalize_observations=normalize_observations,
batch_size=batch_size,
action_noise=action_noise,
critic_l2_reg=critic_l2_reg,
classifier_l2_reg=classifier_l2_reg,
actor_lr=actor_lr,
critic_lr=critic_lr,
classifier_lr=classifier_lr,
enable_popart=popart,
clip_norm=clip_norm,
reward_scale=reward_scale,
entropy_coeff=entropy_coeff)
logger.info('Using agent with the following configuration:')
logger.info(str(agent.__dict__.items()))
logger.info('Initialize Discriminator')
reward_giver = TransitionClassifier(env,
adversary_hidden_size,
entcoeff=adversary_entcoeff)
d_adam = MpiAdam(reward_giver.get_trainable_variables())
logger.info('Load Expert Data')
dataset = Mujoco_Dset(expert_path=expert_path,
traj_limitation=-1) # TODO: customize
logger.info('Start training')
with U.single_threaded_session() as sess:
# init agent
agent.initialize(sess)
# tf saver
saver = tf.train.Saver()
# finalize graph
sess.graph.finalize()
learn(
env,
agent,
reward_giver,
dataset,
g_step,
d_step,
d_stepsize=d_stepsize,
timesteps_per_batch=timesteps_per_batch,
nb_train_steps=nb_train_steps,
max_timesteps=max_timesteps,
max_iters=max_iters, # TODO: max_episodes
callback=callback,
d_adam=d_adam,
sess=sess,
saver=saver)
def main(args):
U.make_session(num_cpu=1).__enter__()
set_global_seeds(args.seed)
env = gym.make(args.env_id)
# delay training env
# 返回next_obs,delay_reward(0/累积奖赏),done,info
env = DelayRewardWrapper(env, args.reward_freq, 1000)
# 评估Env，真实Env
eval_env = gym.make(args.env_id)
def policy_fn(name, ob_space, ac_space, reuse=False):
return mlp_policy.MlpPolicy(name=name,
ob_space=ob_space,
ac_space=ac_space,
reuse=reuse,
hid_size=args.policy_hidden_size,
num_hid_layers=2)
# 设置随机种子
env.seed(args.seed)
eval_env.seed(args.seed)
gym.logger.setLevel(logging.WARN)
task_name = get_task_name(args)
args.checkpoint_dir = osp.join(args.checkpoint_dir, task_name)
args.log_dir = osp.join(args.log_dir,
"reward_coeff_" + str(args.reward_coeff),
args.env_id, "seed_" + str(args.seed))
if args.task == 'train':
dataset = Mujoco_Dset(expert_path=args.expert_path,
traj_limitation=args.traj_limitation)
reward_giver = TransitionClassifier(env,
args.adversary_hidden_size,
entcoeff=args.adversary_entcoeff)
train(
env,
eval_env,
args.seed,
policy_fn,
reward_giver,
dataset,
args.algo,
args.g_step,
args.d_step,
args.policy_entcoeff,
args.reward_coeff,
args.num_timesteps,
args.save_per_iter,
args.checkpoint_dir,
args.log_dir,
args.pretrained,
args.BC_max_iter,
args.num_epochs,
args.eval_interval,
args.timesteps_per_batch,
task_name,
)
elif args.task == 'evaluate':
runner(env,
policy_fn,
args.load_model_path,
timesteps_per_batch=args.timesteps_per_batch,
number_trajs=10,
stochastic_policy=args.stochastic_policy,
save=args.save_sample)
else:
raise NotImplementedError
env.close()
