def update():
obs, act, adv, ret, logp_old = [torch.Tensor(x) for x in buf.get()]
# Policy gradient step
_, logp, _ = actor_critic.policy(obs, act)
ent = (-logp).mean() # a sample estimate for entropy
# VPG policy objective
pi_loss = -(logp * adv).mean()
# Policy gradient step
train_pi.zero_grad()
pi_loss.backward()
average_gradients(train_pi.param_groups)
train_pi.step()
# Value function learning
v = actor_critic.value_function(obs)
v_l_old = F.mse_loss(v, ret)
for _ in range(train_v_iters):
# Output from value function graph
v = actor_critic.value_function(obs)
# VPG value objective
v_loss = F.mse_loss(v, ret)
# Value function gradient step
train_v.zero_grad()
v_loss.backward()
average_gradients(train_v.param_groups)
train_v.step()
# Log changes from update
_, logp, _, v = actor_critic(obs, act)
pi_l_new = -(logp * adv).mean()
v_l_new = F.mse_loss(v, ret)
kl = (logp_old - logp).mean() # a sample estimate for KL-divergence
logger.store(
LossPi=pi_loss,
LossV=v_l_old,
KL=kl,
Entropy=ent,
DeltaLossPi=(pi_l_new - pi_loss),
DeltaLossV=(v_l_new - v_l_old),
)
def update():
inputs = [torch.Tensor(x) for x in buf.get()]
obs, act, adv, ret, logp_old = inputs[:-len(buf.sorted_info_keys)]
policy_args = dict(
zip(buf.sorted_info_keys, inputs[-len(buf.sorted_info_keys):]))
# Main outputs from computation graph
_, logp, _, _, d_kl, v = actor_critic(obs, act, **policy_args)
# Prepare hessian func, gradient eval
ratio = (logp - logp_old).exp() # pi(a|s) / pi_old(a|s)
pi_l_old = -(ratio * adv).mean()
v_l_old = F.mse_loss(v, ret)
g = core.flat_grad(pi_l_old,
actor_critic.policy.parameters(),
retain_graph=True)
g = torch.from_numpy(mpi_avg(g.numpy()))
pi_l_old = mpi_avg(pi_l_old.item())
def Hx(x):
hvp = core.hessian_vector_product(d_kl, actor_critic.policy, x)
if damping_coeff > 0:
hvp += damping_coeff * x
return torch.from_numpy(mpi_avg(hvp.numpy()))
# Core calculations for TRPO or NPG
x = cg(Hx, g)
alpha = torch.sqrt(2 * delta / (torch.dot(x, Hx(x)) + EPS))
old_params = parameters_to_vector(actor_critic.policy.parameters())
def set_and_eval(step):
vector_to_parameters(old_params - alpha * x * step,
actor_critic.policy.parameters())
_, logp, _, _, d_kl = actor_critic.policy(obs, act, **policy_args)
ratio = (logp - logp_old).exp()
pi_loss = -(ratio * adv).mean()
return mpi_avg(d_kl.item()), mpi_avg(pi_loss.item())
if algo == "npg":
kl, pi_l_new = set_and_eval(step=1.0)
elif algo == "trpo":
for j in range(backtrack_iters):
kl, pi_l_new = set_and_eval(step=backtrack_coeff**j)
if kl <= delta and pi_l_new <= pi_l_old:
logger.log(
"Accepting new params at step %d of line search." % j)
logger.store(BacktrackIters=j)
break
if j == backtrack_iters - 1:
logger.log("Line search failed! Keeping old params.")
logger.store(BacktrackIters=j)
kl, pi_l_new = set_and_eval(step=0.0)
# Value function updates
for _ in range(train_v_iters):
v = actor_critic.value_function(obs)
v_loss = F.mse_loss(v, ret)
# Value function gradient step
train_vf.zero_grad()
v_loss.backward()
average_gradients(train_vf.param_groups)
train_vf.step()
v = actor_critic.value_function(obs)
v_l_new = F.mse_loss(v, ret)
# Log changes from update
logger.store(
LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old),
)
def update():
temp_get = buf.get()
obs, act, adv, ret, logp_old = [
torch.Tensor(x).to(device) for x in temp_get
]
# Training policy
_, logp, _ = actor_critic.policy(obs, act)
ratio = (logp - logp_old).exp()
min_adv = torch.where(adv > 0, (1 + clip_ratio) * adv,
(1 - clip_ratio) * adv)
pi_l_old = -(torch.min(ratio * adv, min_adv)).mean()
ent = (-logp).mean() # a sample estimate for entropy
for i in range(train_pi_iters):
# Output from policy function graph
_, logp, _ = actor_critic.policy(obs, act)
# PPO policy objective
ratio = (logp - logp_old).exp()
min_adv = torch.where(adv > 0, (1 + clip_ratio) * adv,
(1 - clip_ratio) * adv)
pi_loss = -(torch.min(ratio * adv, min_adv)).mean()
# Policy gradient step
train_pi.zero_grad()
pi_loss.backward()
average_gradients(train_pi.param_groups)
train_pi.step()
_, logp, _ = actor_critic.policy(obs, act)
kl = (logp_old - logp).mean()
kl = mpi_avg(kl.item())
if kl > 1.5 * target_kl:
logger.log(
'Early stopping at step %d due to reaching max kl.' % i)
break
logger.store(StopIter=i)
# Training value function
v = actor_critic.value_function(obs)
v_l_old = F.mse_loss(v, ret)
for _ in range(train_v_iters):
# Output from value function graph
v = actor_critic.value_function(obs)
# PPO value function objective
v_loss = F.mse_loss(v, ret)
# Value function gradient step
train_v.zero_grad()
v_loss.backward()
average_gradients(train_v.param_groups)
train_v.step()
# Log changes from update
_, logp, _, v = actor_critic(obs, act)
ratio = (logp - logp_old).exp()
min_adv = torch.where(adv > 0, (1 + clip_ratio) * adv,
(1 - clip_ratio) * adv)
pi_l_new = -(torch.min(ratio * adv, min_adv)).mean()
v_l_new = F.mse_loss(v, ret)
kl = (logp_old - logp).mean() # a sample estimate for KL-divergence
clipped = (ratio > (1 + clip_ratio)) | (ratio < (1 - clip_ratio))
cf = (clipped.float()).mean()
logger.store(LossPi=pi_l_old,
LossV=v_l_old,
KL=kl,
Entropy=ent,
ClipFrac=cf,
DeltaLossPi=(pi_l_new - pi_l_old),
DeltaLossV=(v_l_new - v_l_old))