Source code for torchrl.modules.planners.mppi
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations
import torch
from tensordict import TensorDict, TensorDictBase
from torch import nn
from torchrl.envs.common import EnvBase
from torchrl.modules.planners.common import MPCPlannerBase
[docs]class MPPIPlanner(MPCPlannerBase):
"""MPPI Planner Module.
Reference:
- Model predictive path integral control using covariance variable importance
sampling. (Williams, G., Aldrich, A., and Theodorou, E. A.) https://arxiv.org/abs/1509.01149
- Temporal Difference Learning for Model Predictive Control
(Hansen N., Wang X., Su H.) https://arxiv.org/abs/2203.04955
This module will perform a MPPI planning step when given a TensorDict
containing initial states.
A call to the module returns the actions that empirically maximised the
returns given a planning horizon
Args:
env (EnvBase): The environment to perform the planning step on (can be
`ModelBasedEnv` or :obj:`EnvBase`).
planning_horizon (int): The length of the simulated trajectories
optim_steps (int): The number of optimization steps used by the MPC
planner
num_candidates (int): The number of candidates to sample from the
Gaussian distributions.
top_k (int): The number of top candidates to use to
update the mean and standard deviation of the Gaussian distribution.
reward_key (str, optional): The key in the TensorDict to use to
retrieve the reward. Defaults to "reward".
action_key (str, optional): The key in the TensorDict to use to store
the action. Defaults to "action"
Examples:
>>> from tensordict import TensorDict
>>> from torchrl.data import Composite, Unbounded
>>> from torchrl.envs.model_based import ModelBasedEnvBase
>>> from tensordict.nn import TensorDictModule
>>> from torchrl.modules import ValueOperator
>>> from torchrl.objectives.value import TDLambdaEstimator
>>> class MyMBEnv(ModelBasedEnvBase):
... def __init__(self, world_model, device="cpu", dtype=None, batch_size=None):
... super().__init__(world_model, device=device, dtype=dtype, batch_size=batch_size)
... self.state_spec = Composite(
... hidden_observation=Unbounded((4,))
... )
... self.observation_spec = Composite(
... hidden_observation=Unbounded((4,))
... )
... self.action_spec = Unbounded((1,))
... self.reward_spec = Unbounded((1,))
...
... def _reset(self, tensordict: TensorDict) -> TensorDict:
... tensordict = TensorDict(
... {},
... batch_size=self.batch_size,
... device=self.device,
... )
... tensordict = tensordict.update(
... self.full_state_spec.rand())
... tensordict = tensordict.update(
... self.full_action_spec.rand())
... tensordict = tensordict.update(
... self.full_observation_spec.rand())
... return tensordict
...
>>> from torchrl.modules import MLP, WorldModelWrapper
>>> import torch.nn as nn
>>> world_model = WorldModelWrapper(
... TensorDictModule(
... MLP(out_features=4, activation_class=nn.ReLU, activate_last_layer=True, depth=0),
... in_keys=["hidden_observation", "action"],
... out_keys=["hidden_observation"],
... ),
... TensorDictModule(
... nn.Linear(4, 1),
... in_keys=["hidden_observation"],
... out_keys=["reward"],
... ),
... )
>>> env = MyMBEnv(world_model)
>>> value_net = nn.Linear(4, 1)
>>> value_net = ValueOperator(value_net, in_keys=["hidden_observation"])
>>> adv = TDLambdaEstimator(
... gamma=0.99,
... lmbda=0.95,
... value_network=value_net,
... )
>>> # Build a planner and use it as actor
>>> planner = MPPIPlanner(
... env,
... adv,
... temperature=1.0,
... planning_horizon=10,
... optim_steps=11,
... num_candidates=7,
... top_k=3)
>>> env.rollout(5, planner)
TensorDict(
fields={
action: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
hidden_observation: Tensor(shape=torch.Size([5, 4]), device=cpu, dtype=torch.float32, is_shared=False),
next: TensorDict(
fields={
done: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False),
hidden_observation: Tensor(shape=torch.Size([5, 4]), device=cpu, dtype=torch.float32, is_shared=False),
reward: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.float32, is_shared=False),
terminated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5]),
device=cpu,
is_shared=False),
terminated: Tensor(shape=torch.Size([5, 1]), device=cpu, dtype=torch.bool, is_shared=False)},
batch_size=torch.Size([5]),
device=cpu,
is_shared=False)
"""
def __init__(
self,
env: EnvBase,
advantage_module: nn.Module,
temperature: float,
planning_horizon: int,
optim_steps: int,
num_candidates: int,
top_k: int,
reward_key: str = ("next", "reward"),
action_key: str = "action",
):
super().__init__(env=env, action_key=action_key)
self.advantage_module = advantage_module
self.planning_horizon = planning_horizon
self.optim_steps = optim_steps
self.num_candidates = num_candidates
self.top_k = top_k
self.reward_key = reward_key
self.register_buffer("temperature", torch.as_tensor(temperature))
[docs] def planning(self, tensordict: TensorDictBase) -> torch.Tensor:
batch_size = tensordict.batch_size
action_shape = (
*batch_size,
self.num_candidates,
self.planning_horizon,
*self.action_spec.shape,
)
action_stats_shape = (
*batch_size,
1,
self.planning_horizon,
*self.action_spec.shape,
)
action_topk_shape = (
*batch_size,
self.top_k,
self.planning_horizon,
*self.action_spec.shape,
)
adv_topk_shape = (
*batch_size,
self.top_k,
1,
1,
)
K_DIM = len(self.action_spec.shape) - 4
expanded_original_tensordict = (
tensordict.unsqueeze(-1)
.expand(*batch_size, self.num_candidates)
.to_tensordict()
)
_action_means = torch.zeros(
*action_stats_shape,
device=tensordict.device,
dtype=self.env.action_spec.dtype,
)
_action_stds = torch.ones_like(_action_means)
container = TensorDict(
{
"tensordict": expanded_original_tensordict,
"stats": TensorDict(
{
"_action_means": _action_means,
"_action_stds": _action_stds,
},
[*batch_size, 1, self.planning_horizon],
),
},
batch_size,
)
for _ in range(self.optim_steps):
actions_means = container.get(("stats", "_action_means"))
actions_stds = container.get(("stats", "_action_stds"))
actions = actions_means + actions_stds * torch.randn(
*action_shape,
device=actions_means.device,
dtype=actions_means.dtype,
)
actions = self.env.action_spec.project(actions)
optim_tensordict = container.get("tensordict").clone()
policy = _PrecomputedActionsSequentialSetter(actions)
optim_tensordict = self.env.rollout(
max_steps=self.planning_horizon,
policy=policy,
auto_reset=False,
tensordict=optim_tensordict,
)
# compute advantage
self.advantage_module(optim_tensordict)
# get advantage of the current state
advantage = optim_tensordict["advantage"][..., :1, :]
# get top-k trajectories
_, top_k = advantage.topk(self.top_k, dim=K_DIM)
# get omega weights for each top-k trajectory
vals = advantage.gather(K_DIM, top_k.expand(adv_topk_shape))
Omegas = (self.temperature * vals).exp()
# gather best actions
best_actions = actions.gather(K_DIM, top_k.expand(action_topk_shape))
# compute weighted average
_action_means = (Omegas * best_actions).sum(
dim=K_DIM, keepdim=True
) / Omegas.sum(K_DIM, True)
_action_stds = (
(Omegas * (best_actions - _action_means).pow(2)).sum(
dim=K_DIM, keepdim=True
)
/ Omegas.sum(K_DIM, True)
).sqrt()
container.set_(("stats", "_action_means"), _action_means)
container.set_(("stats", "_action_stds"), _action_stds)
action_means = container.get(("stats", "_action_means"))
return action_means[..., 0, 0, :]
class _PrecomputedActionsSequentialSetter:
def __init__(self, actions):
self.actions = actions
self.cmpt = 0
def __call__(self, tensordict):
# checks that the step count is lower or equal to the horizon
if self.cmpt >= self.actions.shape[-2]:
raise ValueError("Precomputed actions sequence is too short")
tensordict = tensordict.set("action", self.actions[..., self.cmpt, :])
self.cmpt += 1
return tensordict
