DDPGLoss

class torchrl.objectives.DDPGLoss(*args, **kwargs)

The DDPG Loss class.

Parameters:

• actor_network (TensorDictModule) – a policy operator.

• value_network (TensorDictModule) – a Q value operator.

• loss_function (str) – loss function for the value discrepancy. Can be one of “l1”, “l2” or “smooth_l1”.

• delay_actor (bool, optional) – whether to separate the target actor networks from the actor networks used for data collection. Default is False.

• delay_value (bool, optional) – whether to separate the target value networks from the value networks used for data collection. Default is True.

• separate_losses (bool, optional) – if True, shared parameters between policy and critic will only be trained on the policy loss. Defaults to False, ie. gradients are propagated to shared parameters for both policy and critic losses.

Examples

>>> import torch
>>> from torch import nn
>>> from torchrl.data import BoundedTensorSpec
>>> from torchrl.modules.tensordict_module.actors import Actor, ValueOperator
>>> from torchrl.objectives.ddpg import DDPGLoss
>>> from tensordict.tensordict import TensorDict
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> actor = Actor(spec=spec, module=nn.Linear(n_obs, n_act))
>>> class ValueClass(nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.linear = nn.Linear(n_obs + n_act, 1)
...     def forward(self, obs, act):
...         return self.linear(torch.cat([obs, act], -1))
>>> module = ValueClass()
>>> value = ValueOperator(
...     module=module,
...     in_keys=["observation", "action"])
>>> loss = DDPGLoss(actor, value)
>>> batch = [2, ]
>>> data = TensorDict({
...        "observation": torch.randn(*batch, n_obs),
...        "action": spec.rand(batch),
...        ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool),
...        ("next", "reward"): torch.randn(*batch, 1),
...        ("next", "observation"): torch.randn(*batch, n_obs),
...    }, batch)
>>> loss(data)
TensorDict(
    fields={
        loss_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        loss_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        pred_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        pred_value_max: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        target_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        target_value_max: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

This class is compatible with non-tensordict based modules too and can be used without recurring to any tensordict-related primitive. In this case, the expected keyword arguments are: ["next_reward", "next_done"] + in_keys of the actor_network and value_network. The return value is a tuple of tensors in the following order: ["loss_actor", "loss_value", "pred_value", "target_value", "pred_value_max", "target_value_max"]

Examples

>>> import torch
>>> from torch import nn
>>> from torchrl.data import BoundedTensorSpec
>>> from torchrl.modules.tensordict_module.actors import Actor, ValueOperator
>>> from torchrl.objectives.ddpg import DDPGLoss
>>> _ = torch.manual_seed(42)
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> actor = Actor(spec=spec, module=nn.Linear(n_obs, n_act))
>>> class ValueClass(nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.linear = nn.Linear(n_obs + n_act, 1)
...     def forward(self, obs, act):
...         return self.linear(torch.cat([obs, act], -1))
>>> module = ValueClass()
>>> value = ValueOperator(
...     module=module,
...     in_keys=["observation", "action"])
>>> loss = DDPGLoss(actor, value)
>>> loss_actor, loss_value, pred_value, target_value, pred_value_max, target_value_max = loss(
...     observation=torch.randn(n_obs),
...     action=spec.rand(),
...     next_done=torch.zeros(1, dtype=torch.bool),
...     next_observation=torch.randn(n_obs),
...     next_reward=torch.randn(1))
>>> loss_actor.backward()

The output keys can also be filtered using the DDPGLoss.select_out_keys() method.

Examples

>>> loss.select_out_keys('loss_actor', 'loss_value')
>>> loss_actor, loss_value = loss(
...     observation=torch.randn(n_obs),
...     action=spec.rand(),
...     next_done=torch.zeros(1, dtype=torch.bool),
...     next_observation=torch.randn(n_obs),
...     next_reward=torch.randn(1))
>>> loss_actor.backward()

forward(tensordict: TensorDictBase) → TensorDict

Computes the DDPG losses given a tensordict sampled from the replay buffer.

This function will also write a “td_error” key that can be used by prioritized replay buffers to assign

a priority to items in the tensordict.

Parameters:

tensordict (TensorDictBase) – a tensordict with keys [“done”, “reward”] and the in_keys of the actor and value networks.

Returns:

a tuple of 2 tensors containing the DDPG loss.

make_value_estimator(value_type: Optional[ValueEstimators] = None, **hyperparams)

Value-function constructor.

If the non-default value function is wanted, it must be built using this method.

Parameters:

• value_type (ValueEstimators) – A ValueEstimators enum type indicating the value function to use. If none is provided, the default stored in the default_value_estimator attribute will be used. The resulting value estimator class will be registered in self.value_type, allowing future refinements.

• **hyperparams – hyperparameters to use for the value function. If not provided, the value indicated by default_value_kwargs() will be used.

Examples

>>> from torchrl.objectives import DQNLoss
>>> # initialize the DQN loss
>>> actor = torch.nn.Linear(3, 4)
>>> dqn_loss = DQNLoss(actor, action_space="one-hot")
>>> # updating the parameters of the default value estimator
>>> dqn_loss.make_value_estimator(gamma=0.9)
>>> dqn_loss.make_value_estimator(
...     ValueEstimators.TD1,
...     gamma=0.9)
>>> # if we want to change the gamma value
>>> dqn_loss.make_value_estimator(dqn_loss.value_type, gamma=0.9)