51c55d4a8a
- Change in isaaclab_env wrapper to explicitly state GPU for each simulation - Removing jax cache to support multi-gpu environment launch in MuJoCo Playground - Removing .train() and .eval() in evaluation and rendering to avoid deadlock in multi-gpu training - Supporting synchronous normalization for multi-gpu training
277 lines
9.0 KiB
Python
277 lines
9.0 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class DistributionalQNetwork(nn.Module):
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def __init__(
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self,
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n_obs: int,
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n_act: int,
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num_atoms: int,
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v_min: float,
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v_max: float,
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hidden_dim: int,
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device: torch.device = None,
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):
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(n_obs + n_act, hidden_dim, device=device),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim // 2, device=device),
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nn.ReLU(),
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nn.Linear(hidden_dim // 2, hidden_dim // 4, device=device),
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nn.ReLU(),
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nn.Linear(hidden_dim // 4, num_atoms, device=device),
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)
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self.v_min = v_min
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self.v_max = v_max
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self.num_atoms = num_atoms
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def forward(self, obs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor:
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x = torch.cat([obs, actions], 1)
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x = self.net(x)
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return x
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def projection(
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self,
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obs: torch.Tensor,
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actions: torch.Tensor,
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rewards: torch.Tensor,
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bootstrap: torch.Tensor,
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discount: float,
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q_support: torch.Tensor,
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device: torch.device,
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) -> torch.Tensor:
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delta_z = (self.v_max - self.v_min) / (self.num_atoms - 1)
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batch_size = rewards.shape[0]
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target_z = (
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rewards.unsqueeze(1)
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+ bootstrap.unsqueeze(1) * discount.unsqueeze(1) * q_support
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)
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target_z = target_z.clamp(self.v_min, self.v_max)
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b = (target_z - self.v_min) / delta_z
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l = torch.floor(b).long()
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u = torch.ceil(b).long()
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l_mask = torch.logical_and((u > 0), (l == u))
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u_mask = torch.logical_and((l < (self.num_atoms - 1)), (l == u))
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l = torch.where(l_mask, l - 1, l)
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u = torch.where(u_mask, u + 1, u)
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next_dist = F.softmax(self.forward(obs, actions), dim=1)
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proj_dist = torch.zeros_like(next_dist)
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offset = (
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torch.linspace(
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0, (batch_size - 1) * self.num_atoms, batch_size, device=device
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)
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.unsqueeze(1)
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.expand(batch_size, self.num_atoms)
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.long()
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)
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proj_dist.view(-1).index_add_(
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0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)
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)
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proj_dist.view(-1).index_add_(
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0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)
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)
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return proj_dist
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class Critic(nn.Module):
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def __init__(
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self,
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n_obs: int,
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n_act: int,
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num_atoms: int,
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v_min: float,
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v_max: float,
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hidden_dim: int,
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device: torch.device = None,
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):
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super().__init__()
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self.qnet1 = DistributionalQNetwork(
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n_obs=n_obs,
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n_act=n_act,
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num_atoms=num_atoms,
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v_min=v_min,
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v_max=v_max,
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hidden_dim=hidden_dim,
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device=device,
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)
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self.qnet2 = DistributionalQNetwork(
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n_obs=n_obs,
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n_act=n_act,
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num_atoms=num_atoms,
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v_min=v_min,
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v_max=v_max,
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hidden_dim=hidden_dim,
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device=device,
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)
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self.register_buffer(
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"q_support", torch.linspace(v_min, v_max, num_atoms, device=device)
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)
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self.device = device
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def forward(self, obs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor:
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return self.qnet1(obs, actions), self.qnet2(obs, actions)
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def projection(
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self,
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obs: torch.Tensor,
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actions: torch.Tensor,
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rewards: torch.Tensor,
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bootstrap: torch.Tensor,
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discount: float,
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) -> torch.Tensor:
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"""Projection operation that includes q_support directly"""
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q1_proj = self.qnet1.projection(
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obs,
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actions,
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rewards,
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bootstrap,
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discount,
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self.q_support,
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self.q_support.device,
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)
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q2_proj = self.qnet2.projection(
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obs,
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actions,
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rewards,
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bootstrap,
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discount,
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self.q_support,
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self.q_support.device,
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)
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return q1_proj, q2_proj
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def get_value(self, probs: torch.Tensor) -> torch.Tensor:
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"""Calculate value from logits using support"""
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return torch.sum(probs * self.q_support, dim=1)
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class Actor(nn.Module):
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def __init__(
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self,
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n_obs: int,
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n_act: int,
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num_envs: int,
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init_scale: float,
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hidden_dim: int,
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std_min: float = 0.05,
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std_max: float = 0.8,
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device: torch.device = None,
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):
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super().__init__()
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self.n_act = n_act
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self.net = nn.Sequential(
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nn.Linear(n_obs, hidden_dim, device=device),
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nn.ReLU(),
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nn.Linear(hidden_dim, hidden_dim // 2, device=device),
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nn.ReLU(),
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nn.Linear(hidden_dim // 2, hidden_dim // 4, device=device),
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nn.ReLU(),
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)
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self.fc_mu = nn.Sequential(
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nn.Linear(hidden_dim // 4, n_act, device=device),
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nn.Tanh(),
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)
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nn.init.normal_(self.fc_mu[0].weight, 0.0, init_scale)
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nn.init.constant_(self.fc_mu[0].bias, 0.0)
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noise_scales = (
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torch.rand(num_envs, 1, device=device) * (std_max - std_min) + std_min
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)
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self.register_buffer("noise_scales", noise_scales)
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self.register_buffer("std_min", torch.as_tensor(std_min, device=device))
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self.register_buffer("std_max", torch.as_tensor(std_max, device=device))
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self.n_envs = num_envs
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self.device = device
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def forward(self, obs: torch.Tensor) -> torch.Tensor:
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x = obs
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x = self.net(x)
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action = self.fc_mu(x)
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return action
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def explore(
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self, obs: torch.Tensor, dones: torch.Tensor = None, deterministic: bool = False
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) -> torch.Tensor:
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# If dones is provided, resample noise for environments that are done
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if dones is not None and dones.sum() > 0:
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# Generate new noise scales for done environments (one per environment)
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new_scales = (
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torch.rand(self.n_envs, 1, device=obs.device)
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* (self.std_max - self.std_min)
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+ self.std_min
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)
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# Update only the noise scales for environments that are done
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dones_view = dones.view(-1, 1) > 0
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self.noise_scales.copy_(
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torch.where(dones_view, new_scales, self.noise_scales)
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)
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act = self(obs)
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if deterministic:
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return act
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noise = torch.randn_like(act) * self.noise_scales
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return act + noise
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class MultiTaskActor(Actor):
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def __init__(self, num_tasks: int, task_embedding_dim: int, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.num_tasks = num_tasks
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self.task_embedding_dim = task_embedding_dim
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self.task_embedding = nn.Embedding(
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num_tasks, task_embedding_dim, max_norm=1.0, device=self.device
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)
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def forward(self, obs: torch.Tensor) -> torch.Tensor:
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# TODO: Optimize the code to be compatible with cudagraphs
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# Currently in-place creation of task_indices is not compatible with cudagraphs
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task_ids_one_hot = obs[..., -self.num_tasks :]
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task_indices = torch.argmax(task_ids_one_hot, dim=1)
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task_embeddings = self.task_embedding(task_indices)
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obs = torch.cat([obs[..., : -self.num_tasks], task_embeddings], dim=-1)
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return super().forward(obs)
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class MultiTaskCritic(Critic):
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def __init__(self, num_tasks: int, task_embedding_dim: int, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.num_tasks = num_tasks
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self.task_embedding_dim = task_embedding_dim
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self.task_embedding = nn.Embedding(
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num_tasks, task_embedding_dim, max_norm=1.0, device=self.device
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)
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def forward(self, obs: torch.Tensor, actions: torch.Tensor) -> torch.Tensor:
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# TODO: Optimize the code to be compatible with cudagraphs
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# Currently in-place creation of task_indices is not compatible with cudagraphs
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task_ids_one_hot = obs[..., -self.num_tasks :]
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task_indices = torch.argmax(task_ids_one_hot, dim=1)
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task_embeddings = self.task_embedding(task_indices)
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obs = torch.cat([obs[..., : -self.num_tasks], task_embeddings], dim=-1)
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return super().forward(obs, actions)
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def projection(
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self,
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obs: torch.Tensor,
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actions: torch.Tensor,
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rewards: torch.Tensor,
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bootstrap: torch.Tensor,
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discount: float,
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) -> torch.Tensor:
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task_ids_one_hot = obs[..., -self.num_tasks :]
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task_indices = torch.argmax(task_ids_one_hot, dim=1)
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task_embeddings = self.task_embedding(task_indices)
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obs = torch.cat([obs[..., : -self.num_tasks], task_embeddings], dim=-1)
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return super().projection(obs, actions, rewards, bootstrap, discount)
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