119 lines
3.6 KiB
Python
119 lines
3.6 KiB
Python
"""
|
|
Actor and Critic models for model-free online RL with DIffusion POlicy (DIPO).
|
|
|
|
"""
|
|
|
|
import torch
|
|
import logging
|
|
|
|
log = logging.getLogger(__name__)
|
|
|
|
from model.diffusion.diffusion import DiffusionModel
|
|
from model.diffusion.sampling import make_timesteps
|
|
|
|
|
|
class DIPODiffusion(DiffusionModel):
|
|
|
|
def __init__(
|
|
self,
|
|
actor,
|
|
critic,
|
|
use_ddim=False,
|
|
randn_clip_value=10,
|
|
clamp_action=False,
|
|
min_sampling_denoising_std=0.1,
|
|
**kwargs,
|
|
):
|
|
super().__init__(network=actor, use_ddim=use_ddim, **kwargs)
|
|
assert not self.use_ddim, "DQL does not support DDIM"
|
|
self.critic = critic.to(self.device)
|
|
|
|
# reassign actor
|
|
self.actor = self.network
|
|
|
|
# Minimum std used in denoising process when sampling action - helps exploration
|
|
self.min_sampling_denoising_std = min_sampling_denoising_std
|
|
|
|
# For each denoising step, we clip sampled randn (from standard deviation) such that the sampled action is not too far away from mean
|
|
self.randn_clip_value = randn_clip_value
|
|
|
|
# Whether to clamp sampled action between [-1, 1]
|
|
self.clamp_action = clamp_action
|
|
|
|
def loss_critic(self, obs, next_obs, actions, rewards, dones, gamma):
|
|
|
|
# get current Q-function
|
|
actions_flat = torch.flatten(actions, start_dim=-2)
|
|
current_q1, current_q2 = self.critic(obs, actions_flat)
|
|
|
|
# get next Q-function
|
|
next_actions = self.forward(
|
|
cond=next_obs,
|
|
deterministic=False,
|
|
) # in DiffusionModel, forward() has no gradient, which is desired here.
|
|
next_actions_flat = torch.flatten(next_actions, start_dim=-2)
|
|
next_q1, next_q2 = self.critic(next_obs, next_actions_flat)
|
|
next_q = torch.min(next_q1, next_q2)
|
|
|
|
# terminal state mask
|
|
mask = 1 - dones
|
|
|
|
# flatten
|
|
rewards = rewards.view(-1)
|
|
next_q = next_q.view(-1)
|
|
mask = mask.view(-1)
|
|
|
|
# target value
|
|
target_q = rewards + gamma * next_q * mask
|
|
|
|
# Update critic
|
|
loss_critic = torch.mean((current_q1 - target_q) ** 2) + torch.mean(
|
|
(current_q2 - target_q) ** 2
|
|
)
|
|
|
|
return loss_critic
|
|
|
|
# override
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
cond,
|
|
deterministic=False,
|
|
):
|
|
device = self.betas.device
|
|
B = cond.shape[0]
|
|
if isinstance(cond, dict):
|
|
raise NotImplementedError("Not implemented for images")
|
|
else:
|
|
B = cond.shape[0]
|
|
cond = cond[:, : self.cond_steps]
|
|
|
|
# Loop
|
|
x = torch.randn((B, self.horizon_steps, self.transition_dim), device=device)
|
|
t_all = list(reversed(range(self.denoising_steps)))
|
|
for i, t in enumerate(t_all):
|
|
t_b = make_timesteps(B, t, device)
|
|
mean, logvar = self.p_mean_var(
|
|
x=x,
|
|
t=t_b,
|
|
cond=cond,
|
|
)
|
|
std = torch.exp(0.5 * logvar)
|
|
|
|
# Determine the noise level
|
|
if deterministic and t == 0:
|
|
std = torch.zeros_like(std)
|
|
elif deterministic: # For DDPM, sample with noise
|
|
std = torch.clip(std, min=1e-3)
|
|
else:
|
|
std = torch.clip(std, min=self.min_sampling_denoising_std)
|
|
noise = torch.randn_like(x).clamp_(
|
|
-self.randn_clip_value, self.randn_clip_value
|
|
)
|
|
x = mean + std * noise
|
|
|
|
# clamp action at final step
|
|
if self.clamp_action and i == len(t_all) - 1:
|
|
x = torch.clamp(x, -1, 1)
|
|
return x
|