def learn(args,
env,
policy_func,
dataset,
optim_batch_size=128,
adam_epsilon=1e-5,
optim_stepsize=3e-4):
# ============================== INIT FROM ARGS ==================================
max_iters = args.BC_max_iter
pretrained = args.pretrained
ckpt_dir = args.checkpoint_dir
log_dir = args.log_dir
task_name = args.task_name
val_per_iter = int(max_iters / 10)
pi = policy_func(args, "pi", env) # Construct network for new policy
oldpi = policy_func(args, "oldpi", env)
# placeholder
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
stochastic = U.get_placeholder_cached(name="stochastic")
loss = tf.reduce_mean(tf.square(ac - pi.ac))
var_list = pi.get_trainable_variables()
adam = MpiAdam(var_list, epsilon=adam_epsilon)
lossandgrad = U.function([ob, ac, stochastic],
[loss] + [U.flatgrad(loss, var_list)])
if not pretrained:
writer = U.FileWriter(log_dir)
ep_stats = stats(["Loss"])
U.initialize()
adam.sync()
logger.log("Pretraining with Behavior Cloning...")
for iter_so_far in tqdm(range(int(max_iters + 1))):
ob_expert, ac_expert = dataset.get_next_batch(optim_batch_size,
'train')
loss, g = lossandgrad(ob_expert, ac_expert, True)
adam.update(g, optim_stepsize)
if not pretrained:
ep_stats.add_all_summary(writer, [loss], iter_so_far)
if iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(-1, 'val')
loss, g = lossandgrad(ob_expert, ac_expert, False)
logger.log("Validation:")
logger.log("Loss: %f" % loss)
if not pretrained:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iter_so_far)
if pretrained:
savedir_fname = tempfile.TemporaryDirectory().name
U.save_state(savedir_fname, max_to_keep=args.max_to_keep)
return savedir_fname
def learn(env,
policy_func,
dataset,
pretrained,
optim_batch_size=128,
max_iters=1e4,
adam_epsilon=1e-5,
optim_stepsize=3e-4,
ckpt_dir=None,
log_dir=None,
task_name=None):
val_per_iter = int(max_iters / 10)
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
# placeholder
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
stochastic = U.get_placeholder_cached(name="stochastic")
loss = tf.reduce_mean(tf.square(ac - pi.ac)) #エキスパート行動と方策行動の差の2乗の平均
var_list = pi.get_trainable_variables()
adam = MpiAdam(var_list, epsilon=adam_epsilon)
lossandgrad = U.function([ob, ac, stochastic],
[loss] + [U.flatgrad(loss, var_list)])
#状態，行動，確率的方策（bool）を入力，loss(エキスパート行動と方策行動の差の2乗の平均)andその勾配を出力
if not pretrained:
writer = U.FileWriter(log_dir)
ep_stats = stats(["Loss"])
U.initialize()
adam.sync()
logger.log("Pretraining with Behavior Cloning...")
for iter_so_far in tqdm(range(int(max_iters))):
ob_expert, ac_expert = dataset.get_next_batch(optim_batch_size,
'train')
loss, g = lossandgrad(ob_expert, ac_expert, True)
adam.update(g, optim_stepsize)
if not pretrained:
ep_stats.add_all_summary(writer, [loss], iter_so_far)
if iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(-1, 'val')
loss, g = lossandgrad(ob_expert, ac_expert, False)
logger.log("Validation:")
logger.log("Loss: %f" % loss)
if not pretrained:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iter_so_far)
if pretrained:
savedir_fname = tempfile.TemporaryDirectory().name
U.save_state(savedir_fname, var_list=pi.get_variables())
return savedir_fname
def learn(env,
policy_func,
dataset,
pretrained,
optim_batch_size=128,
max_iters=1e3,
adam_epsilon=1e-6,
optim_stepsize=2e-4,
ckpt_dir=None,
log_dir=None,
task_name=None,
high_level=False):
val_per_iter = int(max_iters / 100)
ob_space = env.observation_space
ac_space = env.action_space
start_time = time.time()
if not high_level:
pi_low = policy_func("pi_low", ob_space, ac_space.spaces[1])
# placeholder
# ob_low = U.get_placeholder_cached(name="ob")
ob_low = pi_low.ob
ac_low = pi_low.pdtype.sample_placeholder([None])
# stochastic_low = U.get_placeholder_cached(name="stochastic")
stochastic_low = pi_low.stochastic
loss_low = tf.reduce_mean(tf.square(ac_low - pi_low.ac))
var_list_low = pi_low.get_trainable_variables()
adam_low = MpiAdam(var_list_low, epsilon=adam_epsilon)
lossandgrad_low = U.function([ob_low, ac_low, stochastic_low],
[loss_low] +
[U.flatgrad(loss_low, var_list_low)])
if not pretrained:
writer = U.FileWriter(log_dir)
ep_stats_low = stats(["Loss_low"])
U.initialize()
adam_low.sync()
logger.log("Pretraining with Behavior Cloning Low...")
for iter_so_far in tqdm(range(int(max_iters))):
ob_expert, ac_expert = dataset.get_next_batch(
optim_batch_size, 'train', high_level)
loss, g = lossandgrad_low(ob_expert, ac_expert, True)
adam_low.update(g, optim_stepsize)
if not pretrained:
ep_stats_low.add_all_summary(writer, [loss], iter_so_far)
if iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(
-1, 'val', high_level)
loss, g = lossandgrad_low(ob_expert, ac_expert, False)
logger.log("Validation:")
logger.log("Loss: %f" % loss)
if not pretrained:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iter_so_far)
if pretrained:
savedir_fname = tempfile.TemporaryDirectory().name
U.save_state(savedir_fname, var_list=pi_low.get_variables())
return savedir_fname
else:
pi_high = policy_func("pi_high", ob_space,
ac_space.spaces[0]) # high -> action_label
# ob_high = U.get_placeholder_cached(name="ob")
ob_high = pi_high.ob
ac_high = pi_high.pdtype.sample_placeholder([None, 1])
onehot_labels = tf.one_hot(indices=tf.cast(ac_high, tf.int32), depth=3)
# stochastic_high = U.get_placeholder_cached(name="stochastic")
stochastic_high = pi_high.stochastic
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
logits=pi_high.logits, labels=onehot_labels)
loss_high = tf.reduce_mean(cross_entropy)
var_list_high = pi_high.get_trainable_variables()
adam_high = MpiAdam(var_list_high, epsilon=adam_epsilon)
lossandgrad_high = U.function([ob_high, ac_high, stochastic_high],
[loss_high] +
[U.flatgrad(loss_high, var_list_high)])
# train high level policy
if not pretrained:
writer = U.FileWriter(log_dir)
# ep_stats_low = stats(["Loss_low"])
ep_stats_high = stats(["loss_high"])
U.initialize()
adam_high.sync()
logger.log("Pretraining with Behavior Cloning High...")
for iter_so_far in tqdm(range(int(max_iters))):
ob_expert, ac_expert = dataset.get_next_batch(
optim_batch_size, 'train', high_level)
loss, g = lossandgrad_high(ob_expert, ac_expert, True)
adam_high.update(g, optim_stepsize)
if not pretrained:
ep_stats_high.add_all_summary(writer, [loss], iter_so_far)
if iter_so_far % val_per_iter == 0:
ob_expert, ac_expert = dataset.get_next_batch(
-1, 'val', high_level)
loss, g = lossandgrad_high(ob_expert, ac_expert, False)
logger.log("Validation:")
logger.log("Loss: %f" % loss)
if not pretrained:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iter_so_far)
if pretrained:
savedir_fname = tempfile.TemporaryDirectory().name
U.save_state(savedir_fname, var_list=pi_high.get_variables())
return savedir_fname
print("--- %s seconds ---" % (time.time() - start_time))
def learn(
env,
policy_func,
discriminator,
expert_dataset,
pretrained,
pretrained_weight,
*,
g_step,
d_step,
episodes_per_batch, # what to train on
dropout_keep_prob,
sequence_size, #rnn parameters
max_kl,
cg_iters,
gamma,
lam, # advantage estimation
entcoeff=0.0,
cg_damping=1e-2,
vf_stepsize=3e-4,
d_stepsize=3e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0, # time constraint
callback=None,
save_per_iter=100,
ckpt_dir=None,
log_dir=None,
load_model_path=None,
task_name=None):
nworkers = MPI.COMM_WORLD.Get_size()
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi",
ob_space,
ac_space,
reuse=(pretrained_weight != None))
oldpi = policy_func("oldpi", ob_space, ac_space)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
entbonus = entcoeff * meanent
vferr = U.mean(tf.square(pi.vpred - ret))
ratio = tf.exp(pi.pd.logp(ac) -
oldpi.pd.logp(ac)) # advantage * pnew / pold
surrgain = U.mean(ratio * atarg)
optimgain = surrgain + entbonus
losses = [optimgain, meankl, entbonus, surrgain, meanent]
loss_names = ["optimgain", "meankl", "entloss", "surrgain", "entropy"]
dist = meankl
all_var_list = pi.get_trainable_variables()
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")
]
d_adam = MpiAdam(discriminator.get_trainable_variables())
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(
[U.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(oldpi.get_variables(), pi.get_variables())
])
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)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
writer = U.FileWriter(log_dir)
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
d_adam.sync()
vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
discriminator,
episodes_per_batch,
stochastic=True,
seq_length=sequence_size)
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
true_rewbuffer = deque(maxlen=40)
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
g_loss_stats = stats(loss_names)
d_loss_stats = stats(discriminator.loss_name)
ep_stats = stats(["True_rewards", "Rewards", "Episode_length"])
# if provide pretrained weight
if pretrained_weight is not None:
U.load_state(pretrained_weight, var_list=pi.get_variables())
# if provieded model path
if load_model_path is not None:
U.load_state(load_model_path)
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
def fisher_vector_product(p):
return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
# ------------------ Update G ------------------
logger.log("Optimizing Policy...")
for _ in range(g_step):
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, "ob_rms"): pi.ob_rms.update(ob)
args = seg["ob"], seg["ac"], atarg
fvpargs = [arr[::5] for arr in args]
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=128):
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(
mbob) # update running mean/std for policy
g = allmean(compute_vflossandgrad(mbob, mbret))
vfadam.update(g, vf_stepsize)
g_losses = meanlosses
for (lossname, lossval) in zip(loss_names, meanlosses):
logger.record_tabular(lossname, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, discriminator.loss_name))
traj_gen, traj_len_gen = seg["ep_trajs"], seg["ep_lens"]
#traj_expert, traj_len_expert = expert_dataset.get_next_traj_batch()
batch_size = len(traj_gen) // d_step
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
for traj_batch, traj_len_batch in dataset.iterbatches(
(traj_gen, traj_len_gen),
include_final_partial_batch=False,
batch_size=batch_size):
traj_expert, traj_len_expert = expert_dataset.get_next_traj_batch(
len(traj_batch))
# update running mean/std for discriminator
ob_batch, _ = traj2trans(traj_batch, traj_len_batch,
ob_space.shape[0])
ob_expert, _ = traj2trans(traj_expert, traj_len_expert,
ob_space.shape[0])
if hasattr(discriminator, "obs_rms"):
discriminator.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
*newlosses, g = discriminator.lossandgrad(traj_batch,
traj_len_batch,
traj_expert,
traj_len_expert,
dropout_keep_prob)
d_adam.update(allmean(g), d_stepsize)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rets = map(flatten_lists, zip(*listoflrpairs))
true_rewbuffer.extend(true_rets)
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank == 0:
logger.dump_tabular()
g_loss_stats.add_all_summary(writer, g_losses, iters_so_far)
d_loss_stats.add_all_summary(writer, np.mean(d_losses, axis=0),
iters_so_far)
ep_stats.add_all_summary(writer, [
np.mean(true_rewbuffer),
np.mean(rewbuffer),
np.mean(lenbuffer)
], iters_so_far)
def learn(
env,
policy_func,
*,
timesteps_per_batch, # timesteps per actor per update
clip_param,
entcoeff, # clipping parameter epsilon, entropy coeff
optim_epochs,
optim_stepsize,
optim_batchsize, # optimization hypers
gamma,
lam, # advantage estimation
max_timesteps=0,
max_episodes=0,
max_iters=0,
max_seconds=0, # time constraint
callback=None, # you can do anything in the callback, since it takes locals(), globals()
adam_epsilon=1e-5,
schedule='constant', # annealing for stepsize parameters (epsilon and adam)
save_per_iter=100,
ckpt_dir=None,
task="train",
sample_stochastic=True,
load_model_path=None,
task_name=None,
max_sample_traj=1500):
print("max_timrsteps", max_timesteps)
print("max_episodes", max_episodes)
print("max_iters", max_iters)
print("max_seconds", max_seconds)
# Setup losses and stuff
# ----------------------------------------
ob_space = env.observation_space
ac_space = env.action_space
pi = policy_func("pi", ob_space,
ac_space) # Construct network for new policy
oldpi = policy_func("oldpi", ob_space, ac_space) # Network for old policy
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
ac = pi.pdtype.sample_placeholder([None])
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration : r_t(\theta)*A_t
surr2 = U.clip(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg #更新則のCLIP項
pol_surr = -U.mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP) 目的関数
vf_loss = U.mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
var_list = pi.get_trainable_variables()
lossandgrad = U.function([ob, ac, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, var_list)])
adam = MpiAdam(var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function([ob, ac, atarg, ret, lrmult], losses)
U.initialize()
adam.sync()
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
timesteps_per_batch,
stochastic=True)
traj_gen = traj_episode_generator(pi,
env,
timesteps_per_batch,
stochastic=sample_stochastic)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
assert sum(
[max_iters > 0, max_timesteps > 0, max_episodes > 0,
max_seconds > 0]) == 1, "Only one time constraint permitted"
if task == 'sample_trajectory':
# not elegant, i know :(
sample_trajectory(load_model_path, max_sample_traj, traj_gen,
task_name, sample_stochastic)
sys.exit()
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
elif max_seconds and time.time() - tstart >= max_seconds:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(1.0 - float(timesteps_so_far) / max_timesteps, 0)
else:
raise NotImplementedError
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
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
d = Dataset(dict(ob=ob, ac=ac, atarg=atarg, vtarg=tdlamret),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new() # set old parameter values to new parameter values
logger.log("Optimizing...")
logger.log(fmt_row(13, loss_names))
# Here we do a bunch of optimization epochs over the data
for _ in range(optim_epochs):
losses = [
] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
#更新部
*newlosses, g = lossandgrad(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
adam.update(g, optim_stepsize * cur_lrmult)
#ADAMでgをアップデート
losses.append(newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
logger.log("Evaluating losses...")
losses = []
for batch in d.iterate_once(optim_batchsize):
newlosses = compute_losses(batch["ob"], batch["ac"],
batch["atarg"], batch["vtarg"],
cur_lrmult)
losses.append(newlosses)
meanlosses, _, _ = mpi_moments(losses, axis=0)
logger.log(fmt_row(13, meanlosses))
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
lrlocal = (seg["ep_lens"], seg["ep_rets"]) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews = map(flatten_lists, zip(*listoflrpairs))
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpThisIter", len(lens))
episodes_so_far += len(lens)
timesteps_so_far += sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
print("... EpisodesSoFar ", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
print("... TimestepsSoFar ", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
print("... TimeElapsed", time.time() - tstart)
if MPI.COMM_WORLD.Get_rank() == 0:
logger.dump_tabular()
def learn(
env,
policy_func,
discriminator,
expert_dataset,
pretrained,
pretrained_weight,
*,
g_step,
d_step,
timesteps_per_batch, # what to train on
max_kl,
cg_iters,
gamma,
lam, # advantage estimation
entcoeff=0.001,
cg_damping=1e-2,
vf_stepsize=3e-4,
d_stepsize=1.5e-4,
vf_iters=3,
max_timesteps=0,
max_episodes=0,
max_iters=0,
max_seconds=0, # time constraint
callback=None,
save_per_iter=100,
ckpt_dir=None,
log_dir=None,
load_model_path=None,
task_name=None,
timesteps_per_actorbatch=16,
clip_param=1e-5,
adam_epsilon=4e-4,
optim_epochs=1,
optim_stepsize=4e-4,
optim_batchsize=16,
schedule='linear'):
nworkers = MPI.COMM_WORLD.Get_size()
print("##### nworkers: ", nworkers)
rank = MPI.COMM_WORLD.Get_rank()
np.set_printoptions(precision=3)
# Setup losses and stuff
# ----------------------------------------
# ob_space = np.array([5*64*64 + 10*64*64 + 11 + 524]) # env.observation_space
# ac_space = np.array([1]) #env.action_space
from gym import spaces
ob_space = spaces.Box(low=-1000,
high=10000,
shape=(5 * 64 * 64 + 10 * 64 * 64 + 11 + 524, ))
ac_space = spaces.Discrete(524)
pi = policy_func("pi",
ob_space,
ac_space,
reuse=(pretrained_weight != None))
oldpi = policy_func("oldpi", ob_space, ac_space)
atarg = tf.placeholder(
dtype=tf.float32,
shape=[None]) # Target advantage function (if applicable)
ret = tf.placeholder(dtype=tf.float32, shape=[None]) # Empirical return
lrmult = tf.placeholder(
name='lrmult', dtype=tf.float32,
shape=[]) # learning rate multiplier, updated with schedule
clip_param = clip_param * lrmult # Annealed cliping parameter epislon
ob = U.get_placeholder_cached(name="ob")
# ob = U.get_placeholder(name="ob", dtype=tf.float32, shape=(None, ob_space[0]))
ac = pi.pdtype.sample_placeholder([None])
# prevac = pi.pdtype.sample_placeholder([None])
prevac_placeholder = U.get_placeholder_cached(name="last_action_one_hot")
kloldnew = oldpi.pd.kl(pi.pd)
ent = pi.pd.entropy()
# ent = pi.pd.entropy_usual() # see how it works, the value is the same
meankl = U.mean(kloldnew)
meanent = U.mean(ent)
# entbonus = entcoeff * meanent
# entcoeff = entcoeff * lrmult + 1e-5
pol_entpen = (-entcoeff) * meanent
ratio = tf.exp(pi.pd.logp(ac) - oldpi.pd.logp(ac)) # pnew / pold
surr1 = ratio * atarg # surrogate from conservative policy iteration
surr2 = tf.clip_by_value(ratio, 1.0 - clip_param,
1.0 + clip_param) * atarg #
pol_surr = -tf.reduce_mean(tf.minimum(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
vf_loss = tf.reduce_mean(tf.square(pi.vpred - ret))
total_loss = pol_surr + pol_entpen + vf_loss
losses = [pol_surr, pol_entpen, vf_loss, meankl, meanent]
loss_names = ["pol_surr", "pol_entpen", "vf_loss", "kl", "ent"]
var_list = pi.get_trainable_variables()
lossandgrad = U.function([ob, ac, prevac_placeholder, atarg, ret, lrmult],
losses + [U.flatgrad(total_loss, var_list)])
g_adam = MpiAdam(var_list, epsilon=adam_epsilon)
assign_old_eq_new = U.function(
[], [],
updates=[
tf.assign(oldv, newv)
for (oldv,
newv) in zipsame(oldpi.get_variables(), pi.get_variables())
])
compute_losses = U.function(
[ob, ac, prevac_placeholder, atarg, ret, lrmult], losses)
# all_var_list = pi.get_trainable_variables()
# 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")]
d_adam = MpiAdam(discriminator.get_trainable_variables())
# vfadam = MpiAdam(vf_var_list)
get_flat = U.GetFlat(var_list)
set_from_flat = U.SetFromFlat(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)
out = np.empty_like(x)
MPI.COMM_WORLD.Allreduce(x, out, op=MPI.SUM)
out /= nworkers
return out
writer = U.FileWriter(log_dir)
U.initialize()
th_init = get_flat()
MPI.COMM_WORLD.Bcast(th_init, root=0)
set_from_flat(th_init)
g_adam.sync()
d_adam.sync()
# vfadam.sync()
print("Init param sum", th_init.sum(), flush=True)
# Prepare for rollouts
# ----------------------------------------
seg_gen = traj_segment_generator(pi,
env,
discriminator,
timesteps_per_batch,
expert_dataset,
stochastic=True)
episodes_so_far = 0
timesteps_so_far = 0
iters_so_far = 0
tstart = time.time()
lenbuffer = deque(maxlen=100) # rolling buffer for episode lengths
rewbuffer = deque(maxlen=100) # rolling buffer for episode rewards
true_rewbuffer = deque(maxlen=100)
assert sum([max_iters > 0, max_timesteps > 0, max_episodes > 0]) == 1
g_loss_stats = stats(loss_names)
d_loss_stats = stats(discriminator.loss_name)
ep_stats = stats(["True_rewards", "Rewards", "Episode_length"])
# # if provide pretrained weight
# if pretrained_weight is not None:
# U.load_state(pretrained_weight, var_list=pi.get_variables())
# # if provieded model path
# if load_model_path is not None:
# U.load_state(load_model_path)
while True:
if callback: callback(locals(), globals())
if max_timesteps and timesteps_so_far >= max_timesteps:
break
elif max_episodes and episodes_so_far >= max_episodes:
break
elif max_iters and iters_so_far >= max_iters:
break
if schedule == 'constant':
cur_lrmult = 1.0
elif schedule == 'linear':
cur_lrmult = max(
1.0 - float(timesteps_so_far) / (max_timesteps + 1e7),
0.1) # make the smallest number as 0.1 instead of 0
else:
raise NotImplementedError
# Save model
if iters_so_far % save_per_iter == 0 and ckpt_dir is not None:
U.save_state(os.path.join(ckpt_dir, task_name),
counter=iters_so_far)
logger.log("********** Iteration %i ************" % iters_so_far)
# def fisher_vector_product(p):
# return allmean(compute_fvp(p, *fvpargs)) + cg_damping * p
# # ------------------ Update G ------------------
logger.log("Optimizing Policy...")
meanlosses = []
for _ in range(g_step):
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, prevac, atarg, tdlamret = seg["ob"], seg["ac"], seg[
"prevac"], seg["adv"], seg["tdlamret"]
vpredbefore = seg[
"vpred"] # predicted value function before udpate
# print("before standardize atarg value: ", atarg)
if atarg.std() != 0:
atarg = (atarg - atarg.mean()) / atarg.std(
) # standardized advantage function estimate
else:
with open("debug.txt", "a+") as f:
print("atarg.std() is equal to 0", atarg, file=f)
# print("atarg value: ", atarg)
# convert prevac to one hot
one_hot_prevac = []
if type(prevac) is np.ndarray:
depth = prevac.size
one_hot_prevac = np.zeros((depth, 524))
one_hot_prevac[np.arange(depth), prevac] = 1
else:
one_hot_prevac = np.zeros(524)
one_hot_prevac[prevac] = 1
one_hot_prevac = [one_hot_prevac]
prevac = one_hot_prevac
d = Dataset(dict(ob=ob,
ac=ac,
prevac=prevac,
atarg=atarg,
vtarg=tdlamret),
shuffle=not pi.recurrent)
optim_batchsize = optim_batchsize or ob.shape[0]
# print("optim_batchsize: ", optim_batchsize)
if hasattr(pi, "ob_rms"):
pi.ob_rms.update(ob) # update running mean/std for policy
assign_old_eq_new(
) # set old parameter values to new parameter values
logger.log(fmt_row(13, loss_names))
for _ in range(optim_epochs):
losses = [
] # list of tuples, each of which gives the loss for a minibatch
for batch in d.iterate_once(optim_batchsize):
*newlosses, g = lossandgrad(batch["ob"], batch["ac"],
batch['prevac'],
batch["atarg"], batch["vtarg"],
cur_lrmult)
g_adam.update(g, optim_stepsize * cur_lrmult) # allmean(g)
x_newlosses = compute_losses(batch["ob"], batch["ac"],
batch["prevac"],
batch["atarg"],
batch["vtarg"], cur_lrmult)
meanlosses = [x_newlosses]
losses.append(x_newlosses)
logger.log(fmt_row(13, np.mean(losses, axis=0)))
# meanlosses = losses
# # logger.log("Evaluating losses...")
# losses = []
# for batch in d.iterate_once(optim_batchsize):
# newlosses = compute_losses(batch["ob"], batch["ac"], batch["prevac"],
# batch["atarg"], batch["vtarg"], cur_lrmult)
# losses.append(newlosses)
# # # meanlosses,_,_ = mpi_moments(losses, axis=0) # it will be useful for multithreading
meanlosses = np.mean(losses, axis=0)
# logger.log(fmt_row(13, meanlosses))
g_losses = meanlosses
for (lossval, name) in zipsame(meanlosses, loss_names):
logger.record_tabular("loss_" + name, lossval)
logger.record_tabular("ev_tdlam_before",
explained_variance(vpredbefore, tdlamret))
# ------------------ Update D ------------------
logger.log("Optimizing Discriminator...")
logger.log(fmt_row(13, discriminator.loss_name))
global UP_TO_STEP
ob_expert, ac_expert, prevac_expert = expert_dataset.get_next_batch(
len(ob), UP_TO_STEP)
batch_size = len(ob) // d_step
d_losses = [
] # list of tuples, each of which gives the loss for a minibatch
for ob_batch, ac_batch, prevac_batch in dataset.iterbatches(
(ob, ac, prevac),
include_final_partial_batch=False,
batch_size=batch_size):
# print("###### len(ob_batch): ", len(ob_batch))
ob_expert, ac_expert, prevac_expert = expert_dataset.get_next_batch(
len(ob_batch), UP_TO_STEP)
# update running mean/std for discriminator
if hasattr(discriminator, "obs_rms"):
discriminator.obs_rms.update(
np.concatenate((ob_batch, ob_expert), 0))
depth = len(ac_batch)
one_hot_ac_batch = np.zeros((depth, 524))
one_hot_ac_batch[np.arange(depth), ac_batch] = 1
# depth = len(prevac_batch)
# one_hot_prevac_batch = np.zeros((depth, 524))
# one_hot_prevac_batch[np.arange(depth), prevac_batch] = 1
depth = len(ac_expert)
one_hot_ac_expert = np.zeros((depth, 524))
one_hot_ac_expert[np.arange(depth), ac_expert] = 1
depth = len(prevac_expert)
one_hot_prevac_expert = np.zeros((depth, 524))
one_hot_prevac_expert[np.arange(depth), prevac_expert] = 1
*newlosses, g = discriminator.lossandgrad(ob_batch,
one_hot_ac_batch,
prevac_batch, ob_expert,
one_hot_ac_expert,
one_hot_prevac_expert)
global LAST_EXPERT_ACC, LAST_EXPERT_LOSS
LAST_EXPERT_ACC = newlosses[5]
LAST_EXPERT_LOSS = newlosses[1]
d_adam.update(g, d_stepsize) # allmean(g)
d_losses.append(newlosses)
logger.log(fmt_row(13, np.mean(d_losses, axis=0)))
lrlocal = (seg["ep_lens"], seg["ep_rets"], seg["ep_true_rets"]
) # local values
listoflrpairs = MPI.COMM_WORLD.allgather(lrlocal) # list of tuples
lens, rews, true_rets = map(flatten_lists, zip(*listoflrpairs))
true_rewbuffer.extend(true_rets)
lenbuffer.extend(lens)
rewbuffer.extend(rews)
logger.record_tabular("EpLenMean", np.mean(lenbuffer))
logger.record_tabular("EpRewMean", np.mean(rewbuffer))
logger.record_tabular("EpTrueRewMean", np.mean(true_rewbuffer))
# logger.record_tabular("EpThisIter", len(lens))
episodes_so_far = len(lens)
timesteps_so_far = sum(lens)
iters_so_far += 1
logger.record_tabular("EpisodesSoFar", episodes_so_far)
logger.record_tabular("TimestepsSoFar", timesteps_so_far)
logger.record_tabular("TimeElapsed", time.time() - tstart)
if rank == 0:
logger.dump_tabular()
g_loss_stats.add_all_summary(writer, g_losses, iters_so_far)
d_loss_stats.add_all_summary(writer, np.mean(d_losses, axis=0),
iters_so_far)
ep_stats.add_all_summary(writer, [
np.mean(true_rewbuffer),
np.mean(rewbuffer),
np.mean(lenbuffer)
], iters_so_far)
global ITER_SOFAR_GLOBAL
ITER_SOFAR_GLOBAL = iters_so_far
# log ac picked
with open('ac.txt', 'a+') as fh:
print(ac, file=fh)