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Source code for torchrl.objectives.cql

# 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 math
import warnings
from copy import deepcopy
from dataclasses import dataclass

from typing import List, Optional, Tuple, Union

import numpy as np
import torch
import torch.nn as nn
from tensordict import TensorDict, TensorDictBase, TensorDictParams
from tensordict.nn import dispatch, TensorDictModule
from tensordict.utils import NestedKey, unravel_key
from torch import Tensor

from torchrl.data.tensor_specs import Composite
from torchrl.data.utils import _find_action_space
from torchrl.envs.utils import ExplorationType, set_exploration_type

from torchrl.modules import ProbabilisticActor, QValueActor
from torchrl.modules.tensordict_module.common import ensure_tensordict_compatible
from torchrl.objectives.common import LossModule
from torchrl.objectives.utils import (
    _cache_values,
    _GAMMA_LMBDA_DEPREC_ERROR,
    _reduce,
    _vmap_func,
    default_value_kwargs,
    distance_loss,
    ValueEstimators,
)

from torchrl.objectives.value import TD0Estimator, TD1Estimator, TDLambdaEstimator


[docs]class CQLLoss(LossModule): """TorchRL implementation of the continuous CQL loss. Presented in "Conservative Q-Learning for Offline Reinforcement Learning" https://arxiv.org/abs/2006.04779 Args: actor_network (ProbabilisticActor): stochastic actor qvalue_network (TensorDictModule or list of TensorDictModule): Q(s, a) parametric model. This module typically outputs a ``"state_action_value"`` entry. If a single instance of `qvalue_network` is provided, it will be duplicated ``N`` times (where ``N=2`` for this loss). If a list of modules is passed, their parameters will be stacked unless they share the same identity (in which case the original parameter will be expanded). .. warning:: When a list of parameters if passed, it will __not__ be compared against the policy parameters and all the parameters will be considered as untied. Keyword args: loss_function (str, optional): loss function to be used with the value function loss. Default is `"smooth_l1"`. alpha_init (float, optional): initial entropy multiplier. Default is 1.0. min_alpha (float, optional): min value of alpha. Default is None (no minimum value). max_alpha (float, optional): max value of alpha. Default is None (no maximum value). action_spec (TensorSpec, optional): the action tensor spec. If not provided and the target entropy is ``"auto"``, it will be retrieved from the actor. fixed_alpha (bool, optional): if ``True``, alpha will be fixed to its initial value. Otherwise, alpha will be optimized to match the 'target_entropy' value. Default is ``False``. target_entropy (float or str, optional): Target entropy for the stochastic policy. Default is "auto", where target entropy is computed as :obj:`-prod(n_actions)`. delay_actor (bool, optional): Whether to separate the target actor networks from the actor networks used for data collection. Default is ``False``. delay_qvalue (bool, optional): Whether to separate the target Q value networks from the Q value networks used for data collection. Default is ``True``. gamma (float, optional): Discount factor. Default is ``None``. temperature (float, optional): CQL temperature. Default is 1.0. min_q_weight (float, optional): Minimum Q weight. Default is 1.0. max_q_backup (bool, optional): Whether to use the max-min Q backup. Default is ``False``. deterministic_backup (bool, optional): Whether to use the deterministic. Default is ``True``. num_random (int, optional): Number of random actions to sample for the CQL loss. Default is 10. with_lagrange (bool, optional): Whether to use the Lagrange multiplier. Default is ``False``. lagrange_thresh (float, optional): Lagrange threshold. Default is 0.0. reduction (str, optional): Specifies the reduction to apply to the output: ``"none"`` | ``"mean"`` | ``"sum"``. ``"none"``: no reduction will be applied, ``"mean"``: the sum of the output will be divided by the number of elements in the output, ``"sum"``: the output will be summed. Default: ``"mean"``. Examples: >>> import torch >>> from torch import nn >>> from torchrl.data import Bounded >>> from torchrl.modules.distributions import NormalParamExtractor, TanhNormal >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator >>> from torchrl.modules.tensordict_module.common import SafeModule >>> from torchrl.objectives.cql import CQLLoss >>> from tensordict import TensorDict >>> n_act, n_obs = 4, 3 >>> spec = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,)) >>> net = nn.Sequential(nn.Linear(n_obs, 2 * n_act), NormalParamExtractor()) >>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"]) >>> actor = ProbabilisticActor( ... module=module, ... in_keys=["loc", "scale"], ... spec=spec, ... distribution_class=TanhNormal) >>> 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() >>> qvalue = ValueOperator( ... module=module, ... in_keys=['observation', 'action']) >>> loss = CQLLoss(actor, qvalue) >>> batch = [2, ] >>> action = spec.rand(batch) >>> data = TensorDict({ ... "observation": torch.randn(*batch, n_obs), ... "action": action, ... ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool), ... ("next", "terminated"): 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={ alpha: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_actor_bc: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_alpha: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_cql: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_qvalue: 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: ``["action", "next_reward", "next_done", "next_terminated"]`` + in_keys of the actor, value, and qvalue network. The return value is a tuple of tensors in the following order: ``["loss_actor", "loss_qvalue", "loss_alpha", "loss_alpha_prime", "alpha", "entropy"]``. Examples: >>> import torch >>> from torch import nn >>> from torchrl.data import Bounded >>> from torchrl.modules.distributions import NormalParamExtractor, TanhNormal >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator >>> from torchrl.modules.tensordict_module.common import SafeModule >>> from torchrl.objectives.cql import CQLLoss >>> _ = torch.manual_seed(42) >>> n_act, n_obs = 4, 3 >>> spec = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,)) >>> net = nn.Sequential(nn.Linear(n_obs, 2 * n_act), NormalParamExtractor()) >>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"]) >>> actor = ProbabilisticActor( ... module=module, ... in_keys=["loc", "scale"], ... spec=spec, ... distribution_class=TanhNormal) >>> 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() >>> qvalue = ValueOperator( ... module=module, ... in_keys=['observation', 'action']) >>> loss = CQLLoss(actor, qvalue) >>> batch = [2, ] >>> action = spec.rand(batch) >>> loss_actor, loss_actor_bc, loss_qvalue, loss_cql, *_ = loss( ... observation=torch.randn(*batch, n_obs), ... action=action, ... next_done=torch.zeros(*batch, 1, dtype=torch.bool), ... next_terminated=torch.zeros(*batch, 1, dtype=torch.bool), ... next_observation=torch.zeros(*batch, n_obs), ... next_reward=torch.randn(*batch, 1)) >>> loss_actor.backward() The output keys can also be filtered using the :meth:`CQLLoss.select_out_keys` method. Examples: >>> _ = loss.select_out_keys('loss_actor', 'loss_qvalue') >>> loss_actor, loss_qvalue = loss( ... observation=torch.randn(*batch, n_obs), ... action=action, ... next_done=torch.zeros(*batch, 1, dtype=torch.bool), ... next_terminated=torch.zeros(*batch, 1, dtype=torch.bool), ... next_observation=torch.zeros(*batch, n_obs), ... next_reward=torch.randn(*batch, 1)) >>> loss_actor.backward() """ @dataclass class _AcceptedKeys: """Maintains default values for all configurable tensordict keys. This class defines which tensordict keys can be set using '.set_keys(key_name=key_value)' and their default values. Attributes: action (NestedKey): The input tensordict key where the action is expected. Defaults to ``"advantage"``. value (NestedKey): The input tensordict key where the state value is expected. Will be used for the underlying value estimator. Defaults to ``"state_value"``. state_action_value (NestedKey): The input tensordict key where the state action value is expected. Defaults to ``"state_action_value"``. log_prob (NestedKey): The input tensordict key where the log probability is expected. Defaults to ``"_log_prob"``. pred_q1 (NestedKey): The input tensordict key where the predicted Q1 values are expected. Defaults to ``"pred_q1"``. pred_q2 (NestedKey): The input tensordict key where the predicted Q2 values are expected. Defaults to ``"pred_q2"``. priority (NestedKey): The input tensordict key where the target priority is written to. Defaults to ``"td_error"``. cql_q1_loss (NestedKey): The input tensordict key where the CQL Q1 loss is expected. Defaults to ``"cql_q1_loss"``. cql_q2_loss (NestedKey): The input tensordict key where the CQL Q2 loss is expected. Defaults to ``"cql_q2_loss"``. reward (NestedKey): The input tensordict key where the reward is expected. Defaults to ``"reward"``. done (NestedKey): The input tensordict key where the done flag is expected. Defaults to ``"done"``. terminated (NestedKey): The input tensordict key where the terminated flag is expected. Defaults to ``"terminated"``. """ action: NestedKey = "action" value: NestedKey = "state_value" state_action_value: NestedKey = "state_action_value" log_prob: NestedKey = "_log_prob" pred_q1: NestedKey = "pred_q1" pred_q2: NestedKey = "pred_q2" priority: NestedKey = "td_error" cql_q1_loss: NestedKey = "cql_q1_loss" cql_q2_loss: NestedKey = "cql_q2_loss" priority: NestedKey = "td_error" reward: NestedKey = "reward" done: NestedKey = "done" terminated: NestedKey = "terminated" default_keys = _AcceptedKeys() default_value_estimator = ValueEstimators.TD0 actor_network: TensorDictModule qvalue_network: TensorDictModule actor_network_params: TensorDictParams qvalue_network_params: TensorDictParams target_actor_network_params: TensorDictParams target_qvalue_network_params: TensorDictParams def __init__( self, actor_network: ProbabilisticActor, qvalue_network: TensorDictModule | List[TensorDictModule], *, loss_function: str = "smooth_l1", alpha_init: float = 1.0, min_alpha: float = None, max_alpha: float = None, action_spec=None, fixed_alpha: bool = False, target_entropy: Union[str, float] = "auto", delay_actor: bool = False, delay_qvalue: bool = True, gamma: float = None, temperature: float = 1.0, min_q_weight: float = 1.0, max_q_backup: bool = False, deterministic_backup: bool = True, num_random: int = 10, with_lagrange: bool = False, lagrange_thresh: float = 0.0, reduction: str = None, ) -> None: self._out_keys = None if reduction is None: reduction = "mean" super().__init__() # Actor self.delay_actor = delay_actor self.convert_to_functional( actor_network, "actor_network", create_target_params=self.delay_actor, ) # Q value self.delay_qvalue = delay_qvalue self.num_qvalue_nets = 2 self.convert_to_functional( qvalue_network, "qvalue_network", self.num_qvalue_nets, create_target_params=self.delay_qvalue, compare_against=list(actor_network.parameters()), ) self.loss_function = loss_function try: device = next(self.parameters()).device except AttributeError: device = torch.device("cpu") self.register_buffer("alpha_init", torch.tensor(alpha_init, device=device)) if bool(min_alpha) ^ bool(max_alpha): min_alpha = min_alpha if min_alpha else 0.0 if max_alpha == 0: raise ValueError("max_alpha must be either None or greater than 0.") max_alpha = max_alpha if max_alpha else 1e9 if min_alpha: self.register_buffer( "min_log_alpha", torch.tensor(min_alpha, device=device).log() ) else: self.min_log_alpha = None if max_alpha: self.register_buffer( "max_log_alpha", torch.tensor(max_alpha, device=device).log() ) else: self.max_log_alpha = None self.fixed_alpha = fixed_alpha if fixed_alpha: self.register_buffer( "log_alpha", torch.tensor(math.log(alpha_init), device=device) ) else: self.register_parameter( "log_alpha", torch.nn.Parameter(torch.tensor(math.log(alpha_init), device=device)), ) self._target_entropy = target_entropy self._action_spec = action_spec self.target_entropy_buffer = None if gamma is not None: raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR) self.temperature = temperature self.min_q_weight = min_q_weight self.max_q_backup = max_q_backup self.deterministic_backup = deterministic_backup self.num_random = num_random self.with_lagrange = with_lagrange if self.with_lagrange: self.target_action_gap = lagrange_thresh self.register_parameter( "log_alpha_prime", torch.nn.Parameter(torch.tensor(math.log(1.0), device=device)), ) self._make_vmap() self.reduction = reduction def _make_vmap(self): self._vmap_qvalue_networkN0 = _vmap_func( self.qvalue_network, (None, 0), randomness=self.vmap_randomness ) self._vmap_qvalue_network00 = _vmap_func( self.qvalue_network, randomness=self.vmap_randomness ) @property def target_entropy(self): target_entropy = self.target_entropy_buffer if target_entropy is None: delattr(self, "target_entropy_buffer") target_entropy = self._target_entropy action_spec = self._action_spec actor_network = self.actor_network device = next(self.parameters()).device if target_entropy == "auto": action_spec = ( action_spec if action_spec is not None else getattr(actor_network, "spec", None) ) if action_spec is None: raise RuntimeError( "Cannot infer the dimensionality of the action. Consider providing " "the target entropy explicitely or provide the spec of the " "action tensor in the actor network." ) if not isinstance(action_spec, Composite): action_spec = Composite({self.tensor_keys.action: action_spec}) if ( isinstance(self.tensor_keys.action, tuple) and len(self.tensor_keys.action) > 1 ): action_container_shape = action_spec[ self.tensor_keys.action[:-1] ].shape else: action_container_shape = action_spec.shape target_entropy = -float( action_spec[self.tensor_keys.action] .shape[len(action_container_shape) :] .numel() ) self.register_buffer( "target_entropy_buffer", torch.tensor(target_entropy, device=device) ) return self.target_entropy_buffer return target_entropy def _forward_value_estimator_keys(self, **kwargs) -> None: if self._value_estimator is not None: self._value_estimator.set_keys( value=self._tensor_keys.value, reward=self.tensor_keys.reward, done=self.tensor_keys.done, terminated=self.tensor_keys.terminated, )
[docs] def make_value_estimator(self, value_type: ValueEstimators = None, **hyperparams): if value_type is None: value_type = self.default_value_estimator self.value_type = value_type # we will take care of computing the next value inside this module value_net = None hp = dict(default_value_kwargs(value_type)) hp.update(hyperparams) if value_type is ValueEstimators.TD1: self._value_estimator = TD1Estimator( **hp, value_network=value_net, ) elif value_type is ValueEstimators.TD0: self._value_estimator = TD0Estimator( **hp, value_network=value_net, ) elif value_type is ValueEstimators.GAE: raise NotImplementedError( f"Value type {value_type} it not implemented for loss {type(self)}." ) elif value_type is ValueEstimators.TDLambda: self._value_estimator = TDLambdaEstimator( **hp, value_network=value_net, ) else: raise NotImplementedError(f"Unknown value type {value_type}") tensor_keys = { "value_target": "value_target", "value": self.tensor_keys.value, "reward": self.tensor_keys.reward, "done": self.tensor_keys.done, "terminated": self.tensor_keys.terminated, } self._value_estimator.set_keys(**tensor_keys)
@property def in_keys(self): keys = [ self.tensor_keys.action, ("next", self.tensor_keys.reward), ("next", self.tensor_keys.done), ("next", self.tensor_keys.terminated), *self.actor_network.in_keys, *[("next", key) for key in self.actor_network.in_keys], *self.qvalue_network.in_keys, ] return list(set(keys)) @property def out_keys(self): if self._out_keys is None: keys = [ "loss_actor", "loss_actor_bc", "loss_qvalue", "loss_cql", "loss_alpha", "alpha", "entropy", ] if self.with_lagrange: keys.append("loss_alpha_prime") self._out_keys = keys return self._out_keys @out_keys.setter def out_keys(self, values): self._out_keys = values
[docs] @dispatch def forward(self, tensordict: TensorDictBase) -> TensorDictBase: q_loss, metadata = self.q_loss(tensordict) cql_loss, cql_metadata = self.cql_loss(tensordict) if self.with_lagrange: alpha_prime_loss, alpha_prime_metadata = self.alpha_prime_loss(tensordict) metadata.update(alpha_prime_metadata) loss_actor_bc, bc_metadata = self.actor_bc_loss(tensordict) loss_actor, actor_metadata = self.actor_loss(tensordict) loss_alpha, alpha_metadata = self.alpha_loss(actor_metadata) metadata.update(bc_metadata) metadata.update(cql_metadata) metadata.update(actor_metadata) metadata.update(alpha_metadata) tensordict.set( self.tensor_keys.priority, metadata.pop("td_error").detach().max(0).values ) out = { "loss_actor": loss_actor, "loss_actor_bc": loss_actor_bc, "loss_qvalue": q_loss, "loss_cql": cql_loss, "loss_alpha": loss_alpha, "alpha": self._alpha, "entropy": -actor_metadata.get(self.tensor_keys.log_prob).mean().detach(), } if self.with_lagrange: out["loss_alpha_prime"] = alpha_prime_loss.mean() return TensorDict(out, [])
@property @_cache_values def _cached_detach_qvalue_params(self): return self.qvalue_network_params.detach() def actor_bc_loss(self, tensordict: TensorDictBase) -> Tensor: with set_exploration_type( ExplorationType.RANDOM ), self.actor_network_params.to_module(self.actor_network): dist = self.actor_network.get_dist( tensordict, ) a_reparm = dist.rsample() log_prob = dist.log_prob(a_reparm) bc_log_prob = dist.log_prob(tensordict.get(self.tensor_keys.action)) bc_actor_loss = self._alpha * log_prob - bc_log_prob bc_actor_loss = _reduce(bc_actor_loss, reduction=self.reduction) metadata = {"bc_log_prob": bc_log_prob.mean().detach()} return bc_actor_loss, metadata def actor_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, dict]: with set_exploration_type( ExplorationType.RANDOM ), self.actor_network_params.to_module(self.actor_network): dist = self.actor_network.get_dist( tensordict, ) a_reparm = dist.rsample() log_prob = dist.log_prob(a_reparm) td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False) if td_q is tensordict: raise RuntimeError td_q.set(self.tensor_keys.action, a_reparm) td_q = self._vmap_qvalue_networkN0( td_q, self._cached_detach_qvalue_params, ) min_q_logprob = ( td_q.get(self.tensor_keys.state_action_value).min(0)[0].squeeze(-1) ) if log_prob.shape != min_q_logprob.shape: raise RuntimeError( f"Losses shape mismatch: {log_prob.shape} and {min_q_logprob.shape}" ) metadata = {} metadata[self.tensor_keys.log_prob] = log_prob.detach() actor_loss = self._alpha * log_prob - min_q_logprob actor_loss = _reduce(actor_loss, reduction=self.reduction) return actor_loss, metadata def _get_policy_actions(self, data, actor_params, num_actions=10): batch_size = data.batch_size batch_size = list(batch_size[:-1]) + [batch_size[-1] * num_actions] in_keys = [unravel_key(key) for key in self.actor_network.in_keys] def filter_and_repeat(name, x): if name in in_keys: return x.repeat_interleave(num_actions, dim=data.ndim - 1) tensordict = data.named_apply( filter_and_repeat, batch_size=batch_size, filter_empty=True ) with torch.no_grad(): with set_exploration_type(ExplorationType.RANDOM), actor_params.to_module( self.actor_network ): dist = self.actor_network.get_dist(tensordict) action = dist.rsample() tensordict.set(self.tensor_keys.action, action) sample_log_prob = dist.log_prob(action) # tensordict.del_("loc") # tensordict.del_("scale") return ( tensordict.select( *self.actor_network.in_keys, self.tensor_keys.action, strict=False ), sample_log_prob, ) def _get_value_v(self, tensordict, _alpha, actor_params, qval_params): tensordict = tensordict.clone(False) # get actions and log-probs with torch.no_grad(): with set_exploration_type(ExplorationType.RANDOM), actor_params.to_module( self.actor_network ): next_tensordict = tensordict.get("next").clone(False) next_dist = self.actor_network.get_dist(next_tensordict) next_action = next_dist.rsample() next_tensordict.set(self.tensor_keys.action, next_action) next_sample_log_prob = next_dist.log_prob(next_action) # get q-values if not self.max_q_backup: next_tensordict_expand = self._vmap_qvalue_networkN0( next_tensordict, qval_params ) next_state_value = next_tensordict_expand.get( self.tensor_keys.state_action_value ).min(0)[0] if ( next_state_value.shape[-len(next_sample_log_prob.shape) :] != next_sample_log_prob.shape ): next_sample_log_prob = next_sample_log_prob.unsqueeze(-1) if not self.deterministic_backup: next_state_value = next_state_value - _alpha * next_sample_log_prob if self.max_q_backup: next_tensordict, _ = self._get_policy_actions( tensordict.get("next").copy(), actor_params, num_actions=self.num_random, ) next_tensordict_expand = self._vmap_qvalue_networkN0( next_tensordict, qval_params ) state_action_value = next_tensordict_expand.get( self.tensor_keys.state_action_value ) # take max over actions state_action_value = state_action_value.reshape( torch.Size( [self.num_qvalue_nets, *tensordict.shape, self.num_random, -1] ) ).max(-2)[0] # take min over qvalue nets next_state_value = state_action_value.min(0)[0] tensordict.set( ("next", self.value_estimator.tensor_keys.value), next_state_value ) target_value = self.value_estimator.value_estimate(tensordict).squeeze(-1) return target_value def q_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, dict]: # we pass the alpha value to the tensordict. Since it's a scalar, we must erase the batch-size first. target_value = self._get_value_v( tensordict.copy(), self._alpha, self.actor_network_params, self.target_qvalue_network_params, ) tensordict_pred_q = tensordict.select( *self.qvalue_network.in_keys, strict=False ) q_pred = self._vmap_qvalue_networkN0( tensordict_pred_q, self.qvalue_network_params ).get(self.tensor_keys.state_action_value) # write pred values in tensordict for cql loss tensordict.set(self.tensor_keys.pred_q1, q_pred[0]) tensordict.set(self.tensor_keys.pred_q2, q_pred[1]) q_pred = q_pred.squeeze(-1) loss_qval = distance_loss( q_pred, target_value.expand_as(q_pred), loss_function=self.loss_function, ).sum(0) loss_qval = _reduce(loss_qval, reduction=self.reduction) td_error = (q_pred - target_value).pow(2) metadata = {"td_error": td_error.detach()} return loss_qval, metadata def cql_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, dict]: pred_q1 = tensordict.get(self.tensor_keys.pred_q1) pred_q2 = tensordict.get(self.tensor_keys.pred_q2) if pred_q1 is None: raise KeyError( f"Couldn't find the pred_q1 with key {self.tensor_keys.pred_q1} in the input tensordict. " "This could be caused by calling cql_loss method before q_loss method." ) if pred_q2 is None: raise KeyError( f"Couldn't find the pred_q2 with key {self.tensor_keys.pred_q2} in the input tensordict. " "This could be caused by calling cql_loss method before q_loss method." ) random_actions_tensor = pred_q1.new_empty( ( *tensordict.shape[:-1], tensordict.shape[-1] * self.num_random, tensordict[self.tensor_keys.action].shape[-1], ) ).uniform_(-1, 1) curr_actions_td, curr_log_pis = self._get_policy_actions( tensordict.copy(), self.actor_network_params, num_actions=self.num_random, ) new_curr_actions_td, new_log_pis = self._get_policy_actions( tensordict.get("next").copy(), self.actor_network_params, num_actions=self.num_random, ) # process all in one forward pass # stack qvalue params qvalue_params = torch.cat( [ self.qvalue_network_params, self.qvalue_network_params, self.qvalue_network_params, ], 0, ) # select and stack input params # q value random action tensordict_q_random = tensordict.select( *self.actor_network.in_keys, strict=False ) batch_size = tensordict_q_random.batch_size batch_size = list(batch_size[:-1]) + [batch_size[-1] * self.num_random] in_keys = [unravel_key(key) for key in self.actor_network.in_keys] def filter_and_repeat(name, x): if name in in_keys: return x.repeat_interleave( self.num_random, dim=tensordict_q_random.ndim - 1 ) tensordict_q_random = tensordict_q_random.named_apply( filter_and_repeat, batch_size=batch_size, filter_empty=True, ) tensordict_q_random.set(self.tensor_keys.action, random_actions_tensor) cql_tensordict = torch.cat( [ tensordict_q_random.expand( self.num_qvalue_nets, *curr_actions_td.batch_size ), curr_actions_td.expand( self.num_qvalue_nets, *curr_actions_td.batch_size ), new_curr_actions_td.expand( self.num_qvalue_nets, *curr_actions_td.batch_size ), ], 0, ) cql_tensordict = cql_tensordict.contiguous() cql_tensordict_expand = self._vmap_qvalue_network00( cql_tensordict, qvalue_params ) # get q values state_action_value = cql_tensordict_expand.get( self.tensor_keys.state_action_value ) # split q values (q_random, q_curr, q_new,) = state_action_value.split( [ self.num_qvalue_nets, self.num_qvalue_nets, self.num_qvalue_nets, ], dim=0, ) # importance sammpled version random_density = np.log( 0.5 ** curr_actions_td[self.tensor_keys.action].shape[-1] ) cat_q1 = torch.cat( [ q_random[0] - random_density, q_new[0] - new_log_pis.detach().unsqueeze(-1), q_curr[0] - curr_log_pis.detach().unsqueeze(-1), ], -1, ) cat_q2 = torch.cat( [ q_random[1] - random_density, q_new[1] - new_log_pis.detach().unsqueeze(-1), q_curr[1] - curr_log_pis.detach().unsqueeze(-1), ], -1, ) min_qf1_loss = ( torch.logsumexp(cat_q1 / self.temperature, dim=-1) * self.min_q_weight * self.temperature ) min_qf2_loss = ( torch.logsumexp(cat_q2 / self.temperature, dim=-1) * self.min_q_weight * self.temperature ) # Subtract the log likelihood of data cql_q1_loss = min_qf1_loss.flatten() - pred_q1 * self.min_q_weight cql_q2_loss = min_qf2_loss.flatten() - pred_q2 * self.min_q_weight # write cql losses in tensordict for alpha prime loss tensordict.set(self.tensor_keys.cql_q1_loss, cql_q1_loss) tensordict.set(self.tensor_keys.cql_q2_loss, cql_q2_loss) cql_q_loss = (cql_q1_loss + cql_q2_loss).mean(-1) cql_q_loss = _reduce(cql_q_loss, reduction=self.reduction) return cql_q_loss, {} def alpha_prime_loss(self, tensordict: TensorDictBase) -> Tensor: cql_q1_loss = tensordict.get(self.tensor_keys.cql_q1_loss) cql_q2_loss = tensordict.get(self.tensor_keys.cql_q2_loss) if cql_q1_loss is None: raise KeyError( f"Couldn't find the cql_q1_loss with key {self.tensor_keys.cql_q1_loss} in the input tensordict. " "This could be caused by calling alpha_prime_loss method before cql_loss method." ) if cql_q2_loss is None: raise KeyError( f"Couldn't find the cql_q2_loss with key {self.tensor_keys.cql_q2_loss} in the input tensordict. " "This could be caused by calling alpha_prime_loss method before cql_loss method." ) alpha_prime = torch.clamp_max(self.log_alpha_prime.exp(), max=1000000.0) min_qf1_loss = alpha_prime * (cql_q1_loss.mean() - self.target_action_gap) min_qf2_loss = alpha_prime * (cql_q2_loss.mean() - self.target_action_gap) alpha_prime_loss = (-min_qf1_loss - min_qf2_loss) * 0.5 alpha_prime_loss = _reduce(alpha_prime_loss, reduction=self.reduction) return alpha_prime_loss, {} def alpha_loss(self, tensordict: TensorDictBase) -> Tensor: log_pi = tensordict.get(self.tensor_keys.log_prob) if self.target_entropy is not None: # we can compute this loss even if log_alpha is not a parameter alpha_loss = -self.log_alpha * (log_pi.detach() + self.target_entropy) else: # placeholder alpha_loss = torch.zeros_like(log_pi) alpha_loss = _reduce(alpha_loss, reduction=self.reduction) return alpha_loss, {} @property def _alpha(self): if self.min_log_alpha is not None: self.log_alpha.data.clamp_(self.min_log_alpha, self.max_log_alpha) with torch.no_grad(): alpha = self.log_alpha.exp() return alpha
[docs]class DiscreteCQLLoss(LossModule): """TorchRL implementation of the discrete CQL loss. This class implements the discrete conservative Q-learning (CQL) loss function, as presented in the paper "Conservative Q-Learning for Offline Reinforcement Learning" (https://arxiv.org/abs/2006.04779). Args: value_network (Union[QValueActor, nn.Module]): The Q-value network used to estimate state-action values. Keyword Args: loss_function (Optional[str]): The distance function used to calculate the distance between the predicted Q-values and the target Q-values. Defaults to ``l2``. delay_value (bool): Whether to separate the target Q value networks from the Q value networks used for data collection. Default is ``True``. gamma (float, optional): Discount factor. Default is ``None``. action_space: The action space of the environment. If None, it is inferred from the value network. Defaults to None. reduction (str, optional): Specifies the reduction to apply to the output: ``"none"`` | ``"mean"`` | ``"sum"``. ``"none"``: no reduction will be applied, ``"mean"``: the sum of the output will be divided by the number of elements in the output, ``"sum"``: the output will be summed. Default: ``"mean"``. Examples: >>> from torchrl.modules import MLP, QValueActor >>> from torchrl.data import OneHot >>> from torchrl.objectives import DiscreteCQLLoss >>> n_obs, n_act = 4, 3 >>> value_net = MLP(in_features=n_obs, out_features=n_act) >>> spec = OneHot(n_act) >>> actor = QValueActor(value_net, in_keys=["observation"], action_space=spec) >>> loss = DiscreteCQLLoss(actor, action_space=spec) >>> batch = [10,] >>> data = TensorDict({ ... "observation": torch.randn(*batch, n_obs), ... "action": spec.rand(batch), ... ("next", "observation"): torch.randn(*batch, n_obs), ... ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool), ... ("next", "terminated"): torch.zeros(*batch, 1, dtype=torch.bool), ... ("next", "reward"): torch.randn(*batch, 1) ... }, batch) >>> loss(data) TensorDict( fields={ loss_cql: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_qvalue: 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), target_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), td_error: Tensor(shape=torch.Size([1]), 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: ``["observation", "next_observation", "action", "next_reward", "next_done", "next_terminated"]``, and a single loss value is returned. Examples: >>> from torchrl.objectives import DiscreteCQLLoss >>> from torchrl.data import OneHot >>> from torch import nn >>> import torch >>> n_obs = 3 >>> n_action = 4 >>> action_spec = OneHot(n_action) >>> value_network = nn.Linear(n_obs, n_action) # a simple value model >>> dcql_loss = DiscreteCQLLoss(value_network, action_space=action_spec) >>> # define data >>> observation = torch.randn(n_obs) >>> next_observation = torch.randn(n_obs) >>> action = action_spec.rand() >>> next_reward = torch.randn(1) >>> next_done = torch.zeros(1, dtype=torch.bool) >>> next_terminated = torch.zeros(1, dtype=torch.bool) >>> loss_val = dcql_loss( ... observation=observation, ... next_observation=next_observation, ... next_reward=next_reward, ... next_done=next_done, ... next_terminated=next_terminated, ... action=action) """ @dataclass class _AcceptedKeys: """Maintains default values for all configurable tensordict keys. This class defines which tensordict keys can be set using '.set_keys(key_name=key_value)' and their default values. Attributes: value_target (NestedKey): The input tensordict key where the target state value is expected. Will be used for the underlying value estimator Defaults to ``"value_target"``. value (NestedKey): The input tensordict key where the chosen action value is expected. Will be used for the underlying value estimator. Defaults to ``"chosen_action_value"``. action_value (NestedKey): The input tensordict key where the action value is expected. Defaults to ``"action_value"``. action (NestedKey): The input tensordict key where the action is expected. Defaults to ``"action"``. priority (NestedKey): The input tensordict key where the target priority is written to. Defaults to ``"td_error"``. reward (NestedKey): The input tensordict key where the reward is expected. Will be used for the underlying value estimator. Defaults to ``"reward"``. done (NestedKey): The key in the input TensorDict that indicates whether a trajectory is done. Will be used for the underlying value estimator. Defaults to ``"done"``. terminated (NestedKey): The key in the input TensorDict that indicates whether a trajectory is terminated. Will be used for the underlying value estimator. Defaults to ``"terminated"``. pred_val (NestedKey): The key where the predicted value will be written in the input tensordict. This value is subsequently used by cql_loss. Defaults to ``"pred_val"``. """ value_target: NestedKey = "value_target" value: NestedKey = "chosen_action_value" action_value: NestedKey = "action_value" action: NestedKey = "action" priority: NestedKey = "td_error" reward: NestedKey = "reward" done: NestedKey = "done" terminated: NestedKey = "terminated" pred_val: NestedKey = "pred_val" default_keys = _AcceptedKeys() default_value_estimator = ValueEstimators.TD0 out_keys = [ "loss_qvalue", "loss_cql", ] value_network: TensorDictModule value_network_params: TensorDictParams target_value_network_params: TensorDictParams def __init__( self, value_network: Union[QValueActor, nn.Module], *, loss_function: Optional[str] = "l2", delay_value: bool = True, gamma: float = None, action_space=None, reduction: str = None, ) -> None: self._in_keys = None if reduction is None: reduction = "mean" super().__init__() self.delay_value = delay_value value_network = ensure_tensordict_compatible( module=value_network, wrapper_type=QValueActor, action_space=action_space, ) self.convert_to_functional( value_network, "value_network", create_target_params=self.delay_value, ) self.value_network_in_keys = value_network.in_keys self.loss_function = loss_function if action_space is None: # infer from value net if hasattr(value_network, "action_space"): action_space = value_network.spec else: # let's try with action_space then try: action_space = value_network.action_space except AttributeError: raise ValueError(self.ACTION_SPEC_ERROR) if action_space is None: warnings.warn( "action_space was not specified. DiscreteCQLLoss will default to 'one-hot'. " "This behavior will be deprecated soon and a space will have to be passed. " "Check the DiscreteCQLLoss documentation to see how to pass the action space." ) action_space = "one-hot" self.action_space = _find_action_space(action_space) self.reduction = reduction if gamma is not None: raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR) def _forward_value_estimator_keys(self, **kwargs) -> None: if self._value_estimator is not None: self._value_estimator.set_keys( value_target=self.tensor_keys.value_target, value=self._tensor_keys.value, reward=self._tensor_keys.reward, done=self._tensor_keys.done, terminated=self._tensor_keys.terminated, ) self._set_in_keys() def _set_in_keys(self): in_keys = { self.tensor_keys.action, unravel_key(("next", self.tensor_keys.reward)), unravel_key(("next", self.tensor_keys.done)), unravel_key(("next", self.tensor_keys.terminated)), *self.value_network.in_keys, *[unravel_key(("next", key)) for key in self.value_network.in_keys], } self._in_keys = sorted(in_keys, key=str)
[docs] def make_value_estimator(self, value_type: ValueEstimators = None, **hyperparams): if value_type is None: value_type = self.default_value_estimator self.value_type = value_type # we will take care of computing the next value inside this module value_net = deepcopy(self.value_network) self.value_network_params.to_module(value_net, return_swap=False) hp = dict(default_value_kwargs(value_type)) hp.update(hyperparams) if value_type is ValueEstimators.TD1: self._value_estimator = TD1Estimator( **hp, value_network=value_net, ) elif value_type is ValueEstimators.TD0: self._value_estimator = TD0Estimator( **hp, value_network=value_net, ) elif value_type is ValueEstimators.GAE: raise NotImplementedError( f"Value type {value_type} it not implemented for loss {type(self)}." ) elif value_type is ValueEstimators.TDLambda: self._value_estimator = TDLambdaEstimator( **hp, value_network=value_net, ) else: raise NotImplementedError(f"Unknown value type {value_type}") tensor_keys = { "value_target": "value_target", "value": self.tensor_keys.value, "reward": self.tensor_keys.reward, "done": self.tensor_keys.done, "terminated": self.tensor_keys.terminated, } self._value_estimator.set_keys(**tensor_keys)
@property def in_keys(self): if self._in_keys is None: self._set_in_keys() return self._in_keys @in_keys.setter def in_keys(self, values): self._in_keys = values @dispatch def value_loss( self, tensordict: TensorDictBase, ) -> Tuple[torch.Tensor, dict]: td_copy = tensordict.clone(False) with self.value_network_params.to_module(self.value_network): self.value_network(td_copy) action = tensordict.get(self.tensor_keys.action) pred_val = td_copy.get(self.tensor_keys.action_value) if self.action_space == "categorical": if action.shape != pred_val.shape: # unsqueeze the action if it lacks on trailing singleton dim action = action.unsqueeze(-1) pred_val_index = torch.gather(pred_val, -1, index=action).squeeze(-1) else: action = action.to(torch.float) pred_val_index = (pred_val * action).sum(-1) # calculate target value with torch.no_grad(): target_value = self.value_estimator.value_estimate( td_copy, params=self._cached_detached_target_value_params ).squeeze(-1) with torch.no_grad(): td_error = (pred_val_index - target_value).pow(2) td_error = td_error.unsqueeze(-1) tensordict.set( self.tensor_keys.priority, td_error, inplace=True, ) tensordict.set( self.tensor_keys.pred_val, pred_val, inplace=True, ) loss = 0.5 * distance_loss(pred_val_index, target_value, self.loss_function) loss = _reduce(loss, reduction=self.reduction) metadata = { "td_error": td_error.mean(0).detach(), "pred_value": pred_val.mean().detach(), "target_value": target_value.mean().detach(), } return loss, metadata
[docs] @dispatch def forward(self, tensordict: TensorDictBase) -> TensorDict: """Computes the (DQN) CQL loss 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. Args: tensordict (TensorDictBase): a tensordict with keys ["action"] and the in_keys of the value network (observations, "done", "terminated", "reward" in a "next" tensordict). Returns: a tensor containing the CQL loss. """ loss_qval, metadata = self.value_loss(tensordict) loss_cql, _ = self.cql_loss(tensordict) source = { "loss_qvalue": loss_qval, "loss_cql": loss_cql, } source.update(metadata) td_out = TensorDict( source=source, batch_size=[], ) return td_out
@property @_cache_values def _cached_detached_target_value_params(self): return self.target_value_network_params.detach() def cql_loss(self, tensordict): qvalues = tensordict.get(self.tensor_keys.pred_val, default=None) if qvalues is None: raise KeyError( "Couldn't find the predicted qvalue with key {self.tensor_keys.pred_val} in the input tensordict. " "This could be caused by calling cql_loss method before value_loss." ) current_action = tensordict.get(self.tensor_keys.action) logsumexp = torch.logsumexp(qvalues, dim=-1, keepdim=True) if self.action_space == "categorical": if current_action.shape != qvalues.shape: # unsqueeze the action if it lacks on trailing singleton dim current_action = current_action.unsqueeze(-1) q_a = qvalues.gather(-1, current_action) else: q_a = (qvalues * current_action).sum(dim=-1, keepdim=True) loss_cql = (logsumexp - q_a).squeeze(-1) loss_cql = _reduce(loss_cql, reduction=self.reduction) return loss_cql, {}

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