# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from dataclasses import dataclass, MISSING
from typing import Dict, Iterable, Tuple, Type
from tensordict import TensorDictBase
from tensordict.nn import TensorDictModule, TensorDictSequential
from torchrl.data import Composite, Unbounded
from torchrl.modules import EGreedyModule, QValueModule, VDNMixer
from torchrl.objectives import LossModule, QMixerLoss, ValueEstimators
from benchmarl.algorithms.common import Algorithm, AlgorithmConfig
from benchmarl.models.common import ModelConfig
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class Vdn(Algorithm):
"""Vdn (from `https://arxiv.org/abs/1706.05296 <https://arxiv.org/abs/1706.05296>`__).
Args:
loss_function (str): loss function for the value discrepancy. Can be one of "l1", "l2" or "smooth_l1".
delay_value (bool): whether to separate the target value networks from the value networks used for
data collection.
"""
def __init__(self, delay_value: bool, loss_function: str, **kwargs):
super().__init__(**kwargs)
self.delay_value = delay_value
self.loss_function = loss_function
#############################
# Overridden abstract methods
#############################
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def _get_loss(
self, group: str, policy_for_loss: TensorDictModule, continuous: bool
) -> Tuple[LossModule, bool]:
if continuous:
raise NotImplementedError("Vdn is not compatible with continuous actions.")
else:
# Loss
loss_module = QMixerLoss(
policy_for_loss,
self.get_mixer(group),
delay_value=self.delay_value,
loss_function=self.loss_function,
action_space=self.action_spec[group, "action"],
)
loss_module.set_keys(
reward="reward",
action=(group, "action"),
done="done",
terminated="terminated",
action_value=(group, "action_value"),
local_value=(group, "chosen_action_value"),
global_value="chosen_action_value",
priority="td_error",
)
loss_module.make_value_estimator(
ValueEstimators.TD0, gamma=self.experiment_config.gamma
)
return loss_module, True
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def _get_parameters(self, group: str, loss: LossModule) -> Dict[str, Iterable]:
return {
"loss": loss.parameters(),
}
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def _get_policy_for_loss(
self, group: str, model_config: ModelConfig, continuous: bool
) -> TensorDictModule:
n_agents = len(self.group_map[group])
logits_shape = [
*self.action_spec[group, "action"].shape,
self.action_spec[group, "action"].space.n,
]
actor_input_spec = Composite(
{group: self.observation_spec[group].clone().to(self.device)}
)
actor_output_spec = Composite(
{
group: Composite(
{"action_value": Unbounded(shape=logits_shape)},
shape=(n_agents,),
)
}
)
actor_module = model_config.get_model(
input_spec=actor_input_spec,
output_spec=actor_output_spec,
agent_group=group,
input_has_agent_dim=True,
n_agents=n_agents,
centralised=False,
share_params=self.experiment_config.share_policy_params,
device=self.device,
action_spec=self.action_spec,
)
if self.action_mask_spec is not None:
action_mask_key = (group, "action_mask")
else:
action_mask_key = None
value_module = QValueModule(
action_value_key=(group, "action_value"),
action_mask_key=action_mask_key,
out_keys=[
(group, "action"),
(group, "action_value"),
(group, "chosen_action_value"),
],
spec=self.action_spec[group, "action"],
action_space=None,
)
return TensorDictSequential(actor_module, value_module)
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def _get_policy_for_collection(
self, policy_for_loss: TensorDictModule, group: str, continuous: bool
) -> TensorDictModule:
if self.action_mask_spec is not None:
action_mask_key = (group, "action_mask")
else:
action_mask_key = None
greedy = EGreedyModule(
annealing_num_steps=self.experiment_config.get_exploration_anneal_frames(
self.on_policy
),
action_key=(group, "action"),
spec=self.action_spec[(group, "action")],
action_mask_key=action_mask_key,
eps_init=self.experiment_config.exploration_eps_init,
eps_end=self.experiment_config.exploration_eps_end,
device=self.device,
)
return TensorDictSequential(*policy_for_loss, greedy)
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def process_batch(self, group: str, batch: TensorDictBase) -> TensorDictBase:
keys = list(batch.keys(True, True))
done_key = ("next", "done")
terminated_key = ("next", "terminated")
reward_key = ("next", "reward")
if done_key not in keys:
batch.set(
done_key,
batch.get(("next", group, "done")).any(-2),
)
if terminated_key not in keys:
batch.set(
terminated_key,
batch.get(("next", group, "terminated")).any(-2),
)
if reward_key not in keys:
batch.set(
reward_key,
batch.get(("next", group, "reward")).mean(-2),
)
return batch
#####################
# Custom new methods
#####################
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def get_mixer(self, group: str) -> TensorDictModule:
n_agents = len(self.group_map[group])
mixer = TensorDictModule(
module=VDNMixer(
n_agents=n_agents,
device=self.device,
),
in_keys=[(group, "chosen_action_value")],
out_keys=["chosen_action_value"],
)
return mixer
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@dataclass
class VdnConfig(AlgorithmConfig):
"""Configuration dataclass for :class:`~benchmarl.algorithms.Vdn`."""
delay_value: bool = MISSING
loss_function: str = MISSING
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@staticmethod
def associated_class() -> Type[Algorithm]:
return Vdn
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@staticmethod
def supports_continuous_actions() -> bool:
return False
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@staticmethod
def supports_discrete_actions() -> bool:
return True
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@staticmethod
def on_policy() -> bool:
return False