25e4ddb628
solver/policy 边界：去掉 CEM 每轮 cpu().tolist() 和结果过早回 CPU，把 plan/warm-start 保持在 GPU，只在 env.step 前最后一步转成 numpy，同时补 了输入张量的 contiguous 处理；
214 lines
7.9 KiB
Python
214 lines
7.9 KiB
Python
"""Cross Entropy Method solver for model-based planning."""
|
|
|
|
import time
|
|
from typing import Any
|
|
|
|
import gymnasium as gym
|
|
import numpy as np
|
|
import torch
|
|
from gymnasium.spaces import Box
|
|
from loguru import logger as logging
|
|
|
|
from .solver import Costable
|
|
|
|
|
|
class CEMSolver:
|
|
"""Cross Entropy Method solver for action optimization.
|
|
|
|
Args:
|
|
model: World model implementing the Costable protocol.
|
|
batch_size: Number of environments to process in parallel.
|
|
num_samples: Number of action candidates to sample per iteration.
|
|
var_scale: Initial variance scale for the action distribution.
|
|
n_steps: Number of CEM iterations.
|
|
topk: Number of elite samples to keep for distribution update.
|
|
device: Device for tensor computations.
|
|
seed: Random seed for reproducibility.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: Costable,
|
|
batch_size: int = 1,
|
|
num_samples: int = 300,
|
|
var_scale: float = 1,
|
|
n_steps: int = 30,
|
|
topk: int = 30,
|
|
device: str | torch.device = "cpu",
|
|
seed: int = 1234,
|
|
) -> None:
|
|
self.model = model
|
|
self.batch_size = batch_size
|
|
self.var_scale = var_scale
|
|
self.num_samples = num_samples
|
|
self.n_steps = n_steps
|
|
self.topk = topk
|
|
self.device = device
|
|
self.torch_gen = torch.Generator(device=device).manual_seed(seed)
|
|
|
|
def configure(self, *, action_space: gym.Space, n_envs: int, config: Any) -> None:
|
|
"""Configure the solver with environment specifications."""
|
|
self._action_space = action_space
|
|
self._n_envs = n_envs
|
|
self._config = config
|
|
self._action_dim = int(np.prod(action_space.shape[1:]))
|
|
self._configured = True
|
|
|
|
if not isinstance(action_space, Box):
|
|
logging.warning(f"Action space is discrete, got {type(action_space)}. CEMSolver may not work as expected.")
|
|
|
|
@property
|
|
def n_envs(self) -> int:
|
|
"""Number of parallel environments."""
|
|
return self._n_envs
|
|
|
|
@property
|
|
def action_dim(self) -> int:
|
|
"""Flattened action dimension including action_block grouping."""
|
|
return self._action_dim * self._config.action_block
|
|
|
|
@property
|
|
def horizon(self) -> int:
|
|
"""Planning horizon in timesteps."""
|
|
return self._config.horizon
|
|
|
|
def __call__(self, *args: Any, **kwargs: Any) -> dict:
|
|
"""Make solver callable, forwarding to solve()."""
|
|
return self.solve(*args, **kwargs)
|
|
|
|
def init_action_distrib(
|
|
self, actions: torch.Tensor | None = None
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
"""Initialize the action distribution parameters (mean and variance)."""
|
|
device = torch.device(self.device)
|
|
var = self.var_scale * torch.ones(
|
|
[self.n_envs, self.horizon, self.action_dim],
|
|
device=device,
|
|
)
|
|
mean = (
|
|
torch.zeros([self.n_envs, 0, self.action_dim], device=device)
|
|
if actions is None
|
|
else actions
|
|
)
|
|
|
|
remaining = self.horizon - mean.shape[1]
|
|
if remaining > 0:
|
|
new_mean = torch.zeros(
|
|
[self.n_envs, remaining, self.action_dim],
|
|
device=mean.device,
|
|
)
|
|
mean = torch.cat([mean, new_mean], dim=1)
|
|
|
|
return mean, var
|
|
|
|
@torch.inference_mode()
|
|
def solve(
|
|
self, info_dict: dict, init_action: torch.Tensor | None = None
|
|
) -> dict:
|
|
"""Solve the planning problem using Cross Entropy Method."""
|
|
start_time = time.time()
|
|
outputs = {
|
|
"costs": [],
|
|
"mean": [], # History of means
|
|
"var": [], # History of vars
|
|
}
|
|
|
|
# -- initialize the action distribution globally
|
|
mean, var = self.init_action_distrib(init_action)
|
|
if mean.device != torch.device(self.device):
|
|
mean = mean.to(self.device, non_blocking=True)
|
|
if var.device != torch.device(self.device):
|
|
var = var.to(self.device, non_blocking=True)
|
|
|
|
total_envs = self.n_envs
|
|
|
|
# --- Iterate over batches ---
|
|
for start_idx in range(0, total_envs, self.batch_size):
|
|
end_idx = min(start_idx + self.batch_size, total_envs)
|
|
current_bs = end_idx - start_idx
|
|
|
|
# Slice Distribution Parameters for current batch
|
|
batch_mean = mean[start_idx:end_idx]
|
|
batch_var = var[start_idx:end_idx]
|
|
|
|
# Expand Info Dict for current batch
|
|
expanded_infos = {}
|
|
for k, v in info_dict.items():
|
|
# v is shape (n_envs, ...)
|
|
# Slice batch
|
|
v_batch = v[start_idx:end_idx]
|
|
if torch.is_tensor(v):
|
|
if v_batch.device != self.device:
|
|
v_batch = v_batch.to(self.device, non_blocking=True)
|
|
# Add sample dim: (batch, 1, ...)
|
|
v_batch = v_batch.unsqueeze(1)
|
|
# Expand: (batch, num_samples, ...)
|
|
v_batch = v_batch.expand(current_bs, self.num_samples, *v_batch.shape[2:])
|
|
elif isinstance(v, np.ndarray):
|
|
v_batch = np.repeat(v_batch[:, None, ...], self.num_samples, axis=1)
|
|
expanded_infos[k] = v_batch
|
|
|
|
# Optimization Loop
|
|
final_batch_cost = None
|
|
batch_indices = torch.arange(current_bs, device=self.device).unsqueeze(1)
|
|
|
|
for step in range(self.n_steps):
|
|
# Sample action sequences: (Batch, Num_Samples, Horizon, Dim)
|
|
candidates = torch.randn(
|
|
current_bs,
|
|
self.num_samples,
|
|
self.horizon,
|
|
self.action_dim,
|
|
generator=self.torch_gen,
|
|
device=self.device,
|
|
)
|
|
|
|
# Scale and shift: (Batch, N, H, D) * (Batch, 1, H, D) + (Batch, 1, H, D)
|
|
candidates = candidates * batch_var.unsqueeze(1) + batch_mean.unsqueeze(1)
|
|
|
|
# Force the first sample to be the current mean
|
|
candidates[:, 0] = batch_mean
|
|
|
|
current_info = expanded_infos.copy()
|
|
|
|
# Evaluate candidates
|
|
costs = self.model.get_cost(current_info, candidates)
|
|
|
|
assert isinstance(costs, torch.Tensor), f"Expected cost to be a torch.Tensor, got {type(costs)}"
|
|
assert costs.ndim == 2 and costs.shape[0] == current_bs and costs.shape[1] == self.num_samples, (
|
|
f"Expected cost to be of shape ({current_bs}, {self.num_samples}), got {costs.shape}"
|
|
)
|
|
|
|
# Select Top-K
|
|
# topk_vals: (Batch, K), topk_inds: (Batch, K)
|
|
topk_vals, topk_inds = torch.topk(costs, k=self.topk, dim=1, largest=False)
|
|
|
|
# Gather Top-K Candidates
|
|
# We need to select the specific candidates corresponding to topk_inds
|
|
# Indexing: candidates[batch_idx, sample_idx]
|
|
# Result shape: (Batch, K, Horizon, Dim)
|
|
topk_candidates = candidates[batch_indices, topk_inds]
|
|
|
|
# Update Mean and Variance based on Top-K
|
|
batch_mean = topk_candidates.mean(dim=1)
|
|
batch_var = topk_candidates.std(dim=1)
|
|
|
|
# Update final cost for logging
|
|
# We average the cost of the top elites
|
|
final_batch_cost = topk_vals.mean(dim=1).detach()
|
|
|
|
# Write results back to global storage
|
|
mean[start_idx:end_idx] = batch_mean
|
|
var[start_idx:end_idx] = batch_var
|
|
|
|
# Store history/metadata
|
|
outputs["costs"].append(final_batch_cost)
|
|
|
|
outputs["costs"] = torch.cat(outputs["costs"]).cpu().tolist()
|
|
outputs["actions"] = mean.detach()
|
|
outputs["mean"] = [mean.detach()]
|
|
outputs["var"] = [var.detach()]
|
|
|
|
print(f"CEM solve time: {time.time() - start_time:.4f} seconds")
|
|
return outputs
|