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chore: import upstream snapshot with attribution

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2026-07-13 12:36:51 +08:00
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# Copyright 2022 Twitter, Inc and Zhendong Wang.
# SPDX-License-Identifier: Apache-2.0
import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import CosineAnnealingLR
from utils.logger import logger
from agents.diffusion import Diffusion
from agents.model import MLP
from agents.helpers import EMA
class Critic(nn.Module):
def __init__(self, state_dim, action_dim, hidden_dim=256):
super(Critic, self).__init__()
self.q1_model = nn.Sequential(nn.Linear(state_dim + action_dim, hidden_dim),
nn.Mish(),
nn.Linear(hidden_dim, hidden_dim),
nn.Mish(),
nn.Linear(hidden_dim, hidden_dim),
nn.Mish(),
nn.Linear(hidden_dim, 1))
self.q2_model = nn.Sequential(nn.Linear(state_dim + action_dim, hidden_dim),
nn.Mish(),
nn.Linear(hidden_dim, hidden_dim),
nn.Mish(),
nn.Linear(hidden_dim, hidden_dim),
nn.Mish(),
nn.Linear(hidden_dim, 1))
def forward(self, state, action):
x = torch.cat([state, action], dim=-1)
return self.q1_model(x), self.q2_model(x)
def q1(self, state, action):
x = torch.cat([state, action], dim=-1)
return self.q1_model(x)
def q_min(self, state, action):
q1, q2 = self.forward(state, action)
return torch.min(q1, q2)
class Diffusion_QL(object):
def __init__(self,
state_dim,
action_dim,
max_action,
device,
discount,
tau,
max_q_backup=False,
eta=1.0,
beta_schedule='linear',
n_timesteps=100,
ema_decay=0.995,
step_start_ema=1000,
update_ema_every=5,
lr=3e-4,
lr_decay=False,
lr_maxt=1000,
grad_norm=1.0,
):
self.model = MLP(state_dim=state_dim, action_dim=action_dim, device=device)
self.actor = Diffusion(state_dim=state_dim, action_dim=action_dim, model=self.model, max_action=max_action,
beta_schedule=beta_schedule, n_timesteps=n_timesteps,).to(device)
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=lr)
self.lr_decay = lr_decay
self.grad_norm = grad_norm
self.step = 0
self.step_start_ema = step_start_ema
self.ema = EMA(ema_decay)
self.ema_model = copy.deepcopy(self.actor)
self.update_ema_every = update_ema_every
self.critic = Critic(state_dim, action_dim).to(device)
self.critic_target = copy.deepcopy(self.critic)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=3e-4)
if lr_decay:
self.actor_lr_scheduler = CosineAnnealingLR(self.actor_optimizer, T_max=lr_maxt, eta_min=0.)
self.critic_lr_scheduler = CosineAnnealingLR(self.critic_optimizer, T_max=lr_maxt, eta_min=0.)
self.state_dim = state_dim
self.max_action = max_action
self.action_dim = action_dim
self.discount = discount
self.tau = tau
self.eta = eta # q_learning weight
self.device = device
self.max_q_backup = max_q_backup
def step_ema(self):
if self.step < self.step_start_ema:
return
self.ema.update_model_average(self.ema_model, self.actor)
def train(self, replay_buffer, iterations, batch_size=100, log_writer=None):
metric = {'bc_loss': [], 'ql_loss': [], 'actor_loss': [], 'critic_loss': []}
for _ in range(iterations):
# Sample replay buffer / batch
state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)
""" Q Training """
current_q1, current_q2 = self.critic(state, action)
if self.max_q_backup:
next_state_rpt = torch.repeat_interleave(next_state, repeats=10, dim=0)
next_action_rpt = self.ema_model(next_state_rpt)
target_q1, target_q2 = self.critic_target(next_state_rpt, next_action_rpt)
target_q1 = target_q1.view(batch_size, 10).max(dim=1, keepdim=True)[0]
target_q2 = target_q2.view(batch_size, 10).max(dim=1, keepdim=True)[0]
target_q = torch.min(target_q1, target_q2)
else:
next_action = self.ema_model(next_state)
target_q1, target_q2 = self.critic_target(next_state, next_action)
target_q = torch.min(target_q1, target_q2)
target_q = (reward + not_done * self.discount * target_q).detach()
critic_loss = F.mse_loss(current_q1, target_q) + F.mse_loss(current_q2, target_q)
self.critic_optimizer.zero_grad()
critic_loss.backward()
if self.grad_norm > 0:
critic_grad_norms = nn.utils.clip_grad_norm_(self.critic.parameters(), max_norm=self.grad_norm, norm_type=2)
self.critic_optimizer.step()
""" Policy Training """
bc_loss = self.actor.loss(action, state)
new_action = self.actor(state)
q1_new_action, q2_new_action = self.critic(state, new_action)
if np.random.uniform() > 0.5:
q_loss = - q1_new_action.mean() / q2_new_action.abs().mean().detach()
else:
q_loss = - q2_new_action.mean() / q1_new_action.abs().mean().detach()
actor_loss = bc_loss + self.eta * q_loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
if self.grad_norm > 0:
actor_grad_norms = nn.utils.clip_grad_norm_(self.actor.parameters(), max_norm=self.grad_norm, norm_type=2)
self.actor_optimizer.step()
""" Step Target network """
if self.step % self.update_ema_every == 0:
self.step_ema()
for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
self.step += 1
""" Log """
if log_writer is not None:
if self.grad_norm > 0:
log_writer.add_scalar('Actor Grad Norm', actor_grad_norms.max().item(), self.step)
log_writer.add_scalar('Critic Grad Norm', critic_grad_norms.max().item(), self.step)
log_writer.add_scalar('BC Loss', bc_loss.item(), self.step)
log_writer.add_scalar('QL Loss', q_loss.item(), self.step)
log_writer.add_scalar('Critic Loss', critic_loss.item(), self.step)
log_writer.add_scalar('Target_Q Mean', target_q.mean().item(), self.step)
metric['actor_loss'].append(actor_loss.item())
metric['bc_loss'].append(bc_loss.item())
metric['ql_loss'].append(q_loss.item())
metric['critic_loss'].append(critic_loss.item())
if self.lr_decay:
self.actor_lr_scheduler.step()
self.critic_lr_scheduler.step()
return metric
def sample_action(self, state):
state = torch.FloatTensor(state.reshape(1, -1)).to(self.device)
state_rpt = torch.repeat_interleave(state, repeats=50, dim=0)
with torch.no_grad():
action = self.actor.sample(state_rpt)
q_value = self.critic_target.q_min(state_rpt, action).flatten()
idx = torch.multinomial(F.softmax(q_value, dim=0), 1)
return action[idx].cpu().data.numpy().flatten()
def save_model(self, dir, id=None):
if id is not None:
torch.save(self.actor.state_dict(), f'{dir}/actor_{id}.pth')
torch.save(self.critic.state_dict(), f'{dir}/critic_{id}.pth')
else:
torch.save(self.actor.state_dict(), f'{dir}/actor.pth')
torch.save(self.critic.state_dict(), f'{dir}/critic.pth')
def load_model(self, dir, id=None):
if id is not None:
self.actor.load_state_dict(torch.load(f'{dir}/actor_{id}.pth'))
self.critic.load_state_dict(torch.load(f'{dir}/critic_{id}.pth'))
else:
self.actor.load_state_dict(torch.load(f'{dir}/actor.pth'))
self.critic.load_state_dict(torch.load(f'{dir}/critic.pth'))