209 lines
8.0 KiB
Python
209 lines
8.0 KiB
Python
"""Model Predictive Path Integral solver for model-based planning."""
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import time
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from typing import Any
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import gymnasium as gym
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import numpy as np
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import torch
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from gymnasium.spaces import Box
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from loguru import logger as logging
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from .solver import Costable
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class MPPISolver:
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"""Model Predictive Path Integral solver for action optimization.
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Args:
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model: World model implementing the Costable protocol.
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batch_size: Number of environments to process in parallel.
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num_samples: Number of action candidates to sample per iteration.
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var_scale: Initial variance scale for action noise.
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n_steps: Number of MPPI iterations.
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topk: Number of elite samples for weighted averaging.
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temperature: Temperature parameter for softmax weighting.
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device: Device for tensor computations.
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seed: Random seed for reproducibility.
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"""
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def __init__(
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self,
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model: Costable,
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batch_size: int = 1,
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num_samples: int = 300,
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var_scale: float = 1.0,
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n_steps: int = 30,
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topk: int = 30,
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temperature: float = 0.5,
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device: str | torch.device = "cpu",
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seed: int = 1234,
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) -> None:
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self.model = model
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self.batch_size = batch_size
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self.num_samples = num_samples
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self.topk = topk
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self.var_scale = var_scale
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self.n_steps = n_steps
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self.temperature = temperature
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self.device = device
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self.torch_gen = torch.Generator(device=device).manual_seed(seed)
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def configure(self, *, action_space: gym.Space, n_envs: int, config: Any) -> None:
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"""Configure the solver with environment specifications."""
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self._action_space = action_space
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self._n_envs = n_envs
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self._config = config
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self._action_dim = int(np.prod(action_space.shape[1:]))
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self._configured = True
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if not isinstance(action_space, Box):
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logging.warning(
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f"Action space is discrete, got {type(action_space)}. MPPISolver may not work as expected."
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)
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@property
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def n_envs(self) -> int:
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"""Number of parallel environments."""
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return self._n_envs
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@property
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def action_dim(self) -> int:
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"""Flattened action dimension including action_block grouping."""
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return self._action_dim * self._config.action_block
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@property
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def horizon(self) -> int:
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"""Planning horizon in timesteps."""
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return self._config.horizon
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def __call__(self, *args: Any, **kwargs: Any) -> dict:
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"""Make solver callable, forwarding to solve()."""
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return self.solve(*args, **kwargs)
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def init_action_distrib(
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self, actions: torch.Tensor | None = None
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""Initialize the action distribution parameters (mean and variance)."""
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var = self.var_scale * torch.ones([self.n_envs, self.horizon, self.action_dim])
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mean = torch.zeros([self.n_envs, 0, self.action_dim]) if actions is None else actions
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remaining = self.horizon - mean.shape[1]
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if remaining > 0:
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device = mean.device
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new_mean = torch.zeros([self.n_envs, remaining, self.action_dim])
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mean = torch.cat([mean, new_mean], dim=1).to(device)
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return mean, var
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@torch.inference_mode()
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def solve(
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self, info_dict: dict, init_action: torch.Tensor | None = None
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) -> dict:
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"""Solve the planning problem using MPPI."""
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start_time = time.time()
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outputs = {
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"costs": [],
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"mean": [],
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"var": [],
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}
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# -- initialize the action distribution globally
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mean, var = self.init_action_distrib(init_action)
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mean = mean.to(self.device)
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var = var.to(self.device)
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total_envs = self.n_envs
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# --- Iterate over batches ---
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for start_idx in range(0, total_envs, self.batch_size):
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end_idx = min(start_idx + self.batch_size, total_envs)
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current_bs = end_idx - start_idx
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# Slice Distribution Parameters for current batch
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batch_mean = mean[start_idx:end_idx]
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batch_var = var[start_idx:end_idx]
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# Expand Info Dict for current batch (Same as CEM)
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expanded_infos = {}
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for k, v in info_dict.items():
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v_batch = v[start_idx:end_idx]
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if torch.is_tensor(v):
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# Add sample dim: (batch, 1, ...)
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v_batch = v_batch.unsqueeze(1)
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# Expand: (batch, num_samples, ...)
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v_batch = v_batch.expand(current_bs, self.num_samples, *v_batch.shape[2:])
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elif isinstance(v, np.ndarray):
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v_batch = np.repeat(v_batch[:, None, ...], self.num_samples, axis=1)
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expanded_infos[k] = v_batch
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# Optimization Loop
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final_batch_cost = None
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for step in range(self.n_steps):
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# Sample noise: (Batch, Num_Samples, Horizon, Dim)
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noise = torch.randn(
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current_bs,
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self.num_samples,
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self.horizon,
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self.action_dim,
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generator=self.torch_gen,
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device=self.device,
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)
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# MPPI Logic: candidates = mean + noise * sigma
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candidates = batch_mean.unsqueeze(1) + noise * batch_var.unsqueeze(1)
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# Force the first sample to be the current mean (Zero noise)
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candidates[:, 0] = batch_mean
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# Evaluate candidates
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costs = self.model.get_cost(expanded_infos, candidates)
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assert isinstance(costs, torch.Tensor), f"Expected cost to be a torch.Tensor, got {type(costs)}"
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assert costs.ndim == 2 and costs.shape[0] == current_bs and costs.shape[1] == self.num_samples, (
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f"Expected cost to be of shape ({current_bs}, {self.num_samples}), got {costs.shape}"
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)
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# Select Elites (Optional, based on topk)
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if self.topk is not None and self.topk < self.num_samples:
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# topk_vals: (Batch, K), topk_inds: (Batch, K)
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topk_vals, topk_inds = torch.topk(costs, k=self.topk, dim=1, largest=False)
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# Gather Top-K Candidates
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batch_indices = torch.arange(current_bs, device=self.device).unsqueeze(1).expand(-1, self.topk)
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# (Batch, K, Horizon, Dim)
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relevant_candidates = candidates[batch_indices, topk_inds]
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relevant_costs = topk_vals
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else:
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relevant_candidates = candidates
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relevant_costs = costs
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# MPPI Weighting: Softmax(-cost / temperature)
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# Stabilize softmax by subtracting min cost
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min_cost = relevant_costs.min(dim=1, keepdim=True)[0]
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scaled_costs = relevant_costs - min_cost
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weights = torch.softmax(-scaled_costs / self.temperature, dim=1) # (Batch, K)
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# Update Mean: weighted sum of candidates
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# Reshape weights for broadcasting: (Batch, K, 1, 1)
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weights_expanded = weights.unsqueeze(-1).unsqueeze(-1)
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batch_mean = (weights_expanded * relevant_candidates).sum(dim=1)
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# Store average cost of the utilized samples for logging
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final_batch_cost = relevant_costs.mean(dim=1).cpu().tolist()
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# Write results back to global storage
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mean[start_idx:end_idx] = batch_mean
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# We do not update var in standard MPPI
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# Store history/metadata
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outputs["costs"].extend(final_batch_cost)
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outputs["actions"] = mean.detach().cpu()
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outputs["mean"] = [mean.detach().cpu()]
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outputs["var"] = [var.detach().cpu()]
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print(f"MPPI solve time: {time.time() - start_time:.4f} seconds")
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return outputs
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