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sac.py
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sac.py
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import torch
from torch.optim import Adam
import wandb
from common.agent import IsaacAgent
from common.policy import GaussianPolicy
from common.util import grad_false, hard_update, soft_update, update_params
from common.value_function import TwinnedQNetwork
class SACAgent(IsaacAgent):
"""SAC
Tuomas Haarnoja, Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
see https://github.com/haarnoja/sac/blob/master/sac/algos/sac.py
and https://github.com/ku2482/soft-actor-critic.pytorch
"""
def __init__(self, cfg):
super().__init__(cfg)
try:
self.lr = self.agent_cfg["lr"][0]
self.policy_lr = self.agent_cfg["lr"][1]
except:
# bad fix
self.lr = self.agent_cfg["lr"]
self.policy_lr = self.agent_cfg["policy_lr"]
self.value_net_kwargs = self.agent_cfg["value_net_kwargs"]
self.policy_net_kwargs = self.agent_cfg["policy_net_kwargs"]
self.gamma = self.agent_cfg["gamma"]
self.tau = self.agent_cfg["tau"]
self.td_target_update_interval = int(
self.agent_cfg["td_target_update_interval"]
)
self.updates_per_step = self.agent_cfg["updates_per_step"]
self.grad_clip = self.agent_cfg["grad_clip"]
self.entropy_tuning = self.agent_cfg["entropy_tuning"]
self.droprate = self.value_net_kwargs["droprate"]
self.critic = TwinnedQNetwork(
observation_dim=self.observation_dim,
action_dim=self.action_dim,
**self.value_net_kwargs,
).to(self.device)
self.critic_target = TwinnedQNetwork(
observation_dim=self.observation_dim,
action_dim=self.action_dim,
**self.value_net_kwargs,
).to(self.device)
if self.droprate <= 0.0:
self.critic_target = self.critic_target.eval()
hard_update(self.critic_target, self.critic)
grad_false(self.critic_target)
self.policy = GaussianPolicy(
observation_dim=self.observation_dim,
action_dim=self.action_dim,
**self.policy_net_kwargs,
).to(self.device)
# self.q1_optimizer = Adam(self.critic.Q1.parameters(), lr=self.lr)
# self.q2_optimizer = Adam(self.critic.Q2.parameters(), lr=self.lr)
self.q_optimizer = Adam(
self.critic.parameters(), lr=self.lr, betas=[0.9, 0.999]
)
self.policy_optimizer = Adam(
self.policy.parameters(), lr=self.policy_lr, betas=[0.9, 0.999]
)
if self.entropy_tuning:
self.alpha_lr = self.agent_cfg["alpha_lr"]
self.target_entropy = -torch.prod(
torch.Tensor(self.action_shape).to(self.device)
).item() # target entropy = -|A|
self.log_alpha = torch.zeros(
1, requires_grad=True, device=self.device
) # optimize log(alpha), instead of alpha
self.alpha = self.log_alpha.exp()
self.alpha_optimizer = Adam([self.log_alpha], lr=self.alpha_lr)
else:
self.alpha = torch.tensor(self.agent_cfg["alpha"]).to(self.device)
self.learn_steps = 0
def explore(self, s, w): # act with randomness
with torch.no_grad():
a, _, _ = self.policy.sample(s)
return a
def exploit(self, s, w): # act without randomness
with torch.no_grad():
_, _, a = self.policy.sample(s)
return a
def learn(self):
self.learn_steps += 1
if self.learn_steps % self.td_target_update_interval == 0:
soft_update(self.critic_target, self.critic, self.tau)
# if self.per:
# batch, indices, weights = self.replay_buffer.sample(self.mini_batch_size)
# else:
batch = self.replay_buffer.sample(self.mini_batch_size)
weights = 1
q_loss, errors, mean_q1 = self.update_critic(batch, weights)
policy_loss, entropies = self.update_policy(batch, weights)
# update_params(self.policy_optimizer, self.policy, policy_loss, self.grad_clip)
# update_params(self.q1_optimizer, self.critic.Q1, q1_loss, self.grad_clip)
# update_params(self.q2_optimizer, self.critic.Q2, q2_loss, self.grad_clip)
if self.entropy_tuning:
entropy_loss = self.calc_entropy_loss(entropies, weights)
update_params(self.alpha_optimizer, None, entropy_loss)
self.alpha = self.log_alpha.exp()
# if self.per:
# self.replay_buffer.update_priority(indices, errors.cpu().numpy())
if self.learn_steps % self.log_interval == 0:
metrics = {
"loss/Q": q_loss,
"loss/policy": policy_loss,
"state/mean_Q1": mean_q1,
"state/entropy": entropies.detach().mean().item(),
}
if self.entropy_tuning:
metrics.update(
{
"loss/alpha": entropy_loss.detach().item(),
"state/alpha": self.alpha.mean().detach().item(),
}
)
wandb.log(metrics)
def update_critic(self, batch, weights):
(s, f, a, r, s_next, dones) = batch
curr_q1, curr_q2 = self.calc_current_q(s, a)
target_q = self.calc_target_q(r, s_next, dones)
# Critic loss is mean squared TD errors.
q1_loss = torch.mean((curr_q1 - target_q).pow(2) * weights)
q2_loss = torch.mean((curr_q2 - target_q).pow(2) * weights)
q_loss = q1_loss + q2_loss
self.q_optimizer.zero_grad(set_to_none=True)
q_loss.backward()
self.q_optimizer.step()
# TD errors for updating priority weights
errors = torch.abs(curr_q1.detach() - target_q)
# log values to monitor training.
q_loss = q_loss.detach().item()
mean_q1 = curr_q1.detach().mean().item()
return q_loss, errors, mean_q1
def update_policy(self, batch, weights):
(s, f, a, r, s_next, dones) = batch
# We re-sample actions to calculate expectations of Q.
sampled_a, entropy, _ = self.policy.sample(s)
# expectations of Q with clipped double Q technique
q1, q2 = self.critic(s, sampled_a)
if self.droprate > 0.0:
q = 0.5 * (q1 + q2)
else:
q = torch.min(q1, q2)
# Policy objective is maximization of (Q + alpha * entropy).
policy_loss = torch.mean((-q - self.alpha * entropy) * weights)
update_params(self.policy_optimizer, self.policy, policy_loss, self.grad_clip)
return policy_loss.detach().item(), entropy
def calc_entropy_loss(self, entropy, weights):
# Intuitively, we increse alpha when entropy is less than target
# entropy, vice versa.
entropy_loss = -torch.mean(
self.log_alpha * (self.target_entropy - entropy).detach() * weights
)
return entropy_loss
def calc_current_q(self, s, a):
curr_q1, curr_q2 = self.critic(s, a)
return curr_q1, curr_q2
def calc_target_q(self, r, s_next, dones):
with torch.no_grad():
a_next, _, _ = self.policy.sample(s_next)
next_q1, next_q2 = self.critic_target(s_next, a_next)
next_q = torch.min(next_q1, next_q2)
target_q = r + (~dones) * self.gamma * next_q
return target_q
def calc_priority_error(self, batch):
(s, _, a, r, s_next, dones) = batch
with torch.no_grad():
curr_q1, curr_q2 = self.calc_current_q(s, a)
target_q = self.calc_target_q(r, s_next, dones)
error = torch.abs(curr_q1 - target_q).cpu().numpy()
return error
def save_torch_model(self):
from pathlib import Path
path = self.log_path + f"model{self.episodes}/"
Path(path).mkdir(parents=True, exist_ok=True)
self.policy.save(path + "policy")
self.critic.Q1.save(path + "critic1")
self.critic.Q2.save(path + "critic2")
def load_torch_model(self, path):
self.policy.load(path + "policy")
self.critic.Q1.load(path + "critic1")
self.critic.Q2.load(path + "critic2")
hard_update(self.critic_target, self.critic)
grad_false(self.critic_target)