diff --git a/metastable_baselines/projections_orig/__init__.py b/metastable_baselines/projections_orig/__init__.py
deleted file mode 100644
index a578185..0000000
--- a/metastable_baselines/projections_orig/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# Copyright (c) 2021 Robert Bosch GmbH
-# Author: Fabian Otto
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Affero General Public License as published
-# by the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Affero General Public License for more details.
-#
-# You should have received a copy of the GNU Affero General Public License
-# along with this program. If not, see .
diff --git a/metastable_baselines/projections_orig/base_projection_layer.py b/metastable_baselines/projections_orig/base_projection_layer.py
deleted file mode 100644
index 3a881af..0000000
--- a/metastable_baselines/projections_orig/base_projection_layer.py
+++ /dev/null
@@ -1,374 +0,0 @@
-# Copyright (c) 2021 Robert Bosch GmbH
-# Author: Fabian Otto
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Affero General Public License as published
-# by the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Affero General Public License for more details.
-#
-# You should have received a copy of the GNU Affero General Public License
-# along with this program. If not, see .
-
-import copy
-import math
-import torch as ch
-from typing import Tuple, Union
-
-from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
-from trust_region_projections.utils.network_utils import get_optimizer
-from trust_region_projections.utils.projection_utils import gaussian_kl, get_entropy_schedule
-from trust_region_projections.utils.torch_utils import generate_minibatches, select_batch, tensorize
-
-
-def entropy_inequality_projection(policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- beta: Union[float, ch.Tensor]):
- """
- Projects std to satisfy an entropy INEQUALITY constraint.
- Args:
- policy: policy instance
- p: current distribution
- beta: target entropy for EACH std or general bound for all stds
-
- Returns:
- projected std that satisfies the entropy bound
- """
- mean, std = p
- k = std.shape[-1]
- batch_shape = std.shape[:-2]
-
- ent = policy.entropy(p)
- mask = ent < beta
-
- # if nothing has to be projected skip computation
- if (~mask).all():
- return p
-
- alpha = ch.ones(batch_shape, dtype=std.dtype, device=std.device)
- alpha[mask] = ch.exp((beta[mask] - ent[mask]) / k)
-
- proj_std = ch.einsum('ijk,i->ijk', std, alpha)
- return mean, ch.where(mask[..., None, None], proj_std, std)
-
-
-def entropy_equality_projection(policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- beta: Union[float, ch.Tensor]):
- """
- Projects std to satisfy an entropy EQUALITY constraint.
- Args:
- policy: policy instance
- p: current distribution
- beta: target entropy for EACH std or general bound for all stds
-
- Returns:
- projected std that satisfies the entropy bound
- """
- mean, std = p
- k = std.shape[-1]
-
- ent = policy.entropy(p)
- alpha = ch.exp((beta - ent) / k)
- proj_std = ch.einsum('ijk,i->ijk', std, alpha)
- return mean, proj_std
-
-
-def mean_projection(mean: ch.Tensor, old_mean: ch.Tensor, maha: ch.Tensor, eps: ch.Tensor):
- """
- Projects the mean based on the Mahalanobis objective and trust region.
- Args:
- mean: current mean vectors
- old_mean: old mean vectors
- maha: Mahalanobis distance between the two mean vectors
- eps: trust region bound
-
- Returns:
- projected mean that satisfies the trust region
- """
- batch_shape = mean.shape[:-1]
- mask = maha > eps
-
- ################################################################################################################
- # mean projection maha
-
- # if nothing has to be projected skip computation
- if mask.any():
- omega = ch.ones(batch_shape, dtype=mean.dtype, device=mean.device)
- omega[mask] = ch.sqrt(maha[mask] / eps) - 1.
- omega = ch.max(-omega, omega)[..., None]
-
- m = (mean + omega * old_mean) / (1 + omega + 1e-16)
- proj_mean = ch.where(mask[..., None], m, mean)
- else:
- proj_mean = mean
-
- return proj_mean
-
-
-class BaseProjectionLayer(object):
-
- def __init__(self,
- proj_type: str = "",
- mean_bound: float = 0.03,
- cov_bound: float = 1e-3,
- trust_region_coeff: float = 0.0,
- scale_prec: bool = True,
-
- entropy_schedule: Union[None, str] = None,
- action_dim: Union[None, int] = None,
- total_train_steps: Union[None, int] = None,
- target_entropy: float = 0.0,
- temperature: float = 0.5,
- entropy_eq: bool = False,
- entropy_first: bool = False,
-
- do_regression: bool = False,
- regression_iters: int = 1000,
- regression_lr: int = 3e-4,
- optimizer_type_reg: str = "adam",
-
- cpu: bool = True,
- dtype: ch.dtype = ch.float32,
- ):
-
- """
- Base projection layer, which can be used to compute metrics for non-projection approaches.
- Args:
- proj_type: Which type of projection to use. None specifies no projection and uses the TRPO objective.
- mean_bound: projection bound for the step size w.r.t. mean
- cov_bound: projection bound for the step size w.r.t. covariance matrix
- trust_region_coeff: Coefficient for projection regularization loss term.
- scale_prec: If true used mahalanobis distance for projections instead of euclidean with Sigma_old^-1.
- entropy_schedule: Schedule type for entropy projection, one of 'linear', 'exp', None.
- action_dim: number of action dimensions to scale exp decay correctly.
- total_train_steps: total number of training steps to compute appropriate decay over time.
- target_entropy: projection bound for the entropy of the covariance matrix
- temperature: temperature decay for exponential entropy bound
- entropy_eq: Use entropy equality constraints.
- entropy_first: Project entropy before trust region.
- do_regression: Conduct additional regression steps after the the policy steps to match projection and policy.
- regression_iters: Number of regression steps.
- regression_lr: Regression learning rate.
- optimizer_type_reg: Optimizer for regression.
- cpu: Compute on CPU only.
- dtype: Data type to use, either of float32 or float64. The later might be necessary for higher
- dimensions in order to learn the full covariance.
- """
-
- # projection and bounds
- self.proj_type = proj_type
- self.mean_bound = tensorize(mean_bound, cpu=cpu, dtype=dtype)
- self.cov_bound = tensorize(cov_bound, cpu=cpu, dtype=dtype)
- self.trust_region_coeff = trust_region_coeff
- self.scale_prec = scale_prec
-
- # projection utils
- assert (action_dim and total_train_steps) if entropy_schedule else True
- self.entropy_proj = entropy_equality_projection if entropy_eq else entropy_inequality_projection
- self.entropy_schedule = get_entropy_schedule(entropy_schedule, total_train_steps, dim=action_dim)
- self.target_entropy = tensorize(target_entropy, cpu=cpu, dtype=dtype)
- self.entropy_first = entropy_first
- self.entropy_eq = entropy_eq
- self.temperature = temperature
- self._initial_entropy = None
-
- # regression
- self.do_regression = do_regression
- self.regression_iters = regression_iters
- self.lr_reg = regression_lr
- self.optimizer_type_reg = optimizer_type_reg
-
- def __call__(self, policy, p: Tuple[ch.Tensor, ch.Tensor], q, step, *args, **kwargs):
- # entropy_bound = self.policy.entropy(q) - self.target_entropy
- entropy_bound = self.entropy_schedule(self.initial_entropy, self.target_entropy, self.temperature,
- step) * p[0].new_ones(p[0].shape[0])
- return self._projection(policy, p, q, self.mean_bound, self.cov_bound, entropy_bound, **kwargs)
-
- def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
- """
- Hook for implementing the specific trust region projection
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- eps: mean trust region bound
- eps_cov: covariance trust region bound
- **kwargs:
-
- Returns:
- projected
- """
- return p
-
- # @final
- def _projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, beta: ch.Tensor, **kwargs):
- """
- Template method with hook _trust_region_projection() to encode specific functionality.
- (Optional) entropy projection is executed before or after as specified by entropy_first.
- Do not override this. For Python >= 3.8 you can use the @final decorator to enforce not overwriting.
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- eps: mean trust region bound
- eps_cov: covariance trust region bound
- beta: entropy bound
- **kwargs:
-
- Returns:
- projected mean, projected std
- """
-
- ####################################################################################################################
- # entropy projection in the beginning
- if self.entropy_first:
- p = self.entropy_proj(policy, p, beta)
-
- ####################################################################################################################
- # trust region projection for mean and cov bounds
- proj_mean, proj_std = self._trust_region_projection(policy, p, q, eps, eps_cov, **kwargs)
-
- ####################################################################################################################
- # entropy projection in the end
- if self.entropy_first:
- return proj_mean, proj_std
-
- return self.entropy_proj(policy, (proj_mean, proj_std), beta)
-
- @property
- def initial_entropy(self):
- return self._initial_entropy
-
- @initial_entropy.setter
- def initial_entropy(self, entropy):
- if self.initial_entropy is None:
- self._initial_entropy = entropy
-
- def trust_region_value(self, policy, p, q):
- """
- Computes the KL divergence between two Gaussian distributions p and q.
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- Returns:
- Mean and covariance part of the trust region metric.
- """
- return gaussian_kl(policy, p, q)
-
- def get_trust_region_loss(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- proj_p: Tuple[ch.Tensor, ch.Tensor]):
- """
- Compute the trust region loss to ensure policy output and projection stay close.
- Args:
- policy: policy instance
- proj_p: projected distribution
- p: predicted distribution from network output
-
- Returns:
- trust region loss
- """
- p_target = (proj_p[0].detach(), proj_p[1].detach())
- mean_diff, cov_diff = self.trust_region_value(policy, p, p_target)
-
- delta_loss = (mean_diff + cov_diff if policy.contextual_std else mean_diff).mean()
-
- return delta_loss * self.trust_region_coeff
-
- def compute_metrics(self, policy, p, q) -> dict:
- """
- Returns dict with constraint metrics.
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
-
- Returns:
- dict with constraint metrics
- """
- with ch.no_grad():
- entropy_old = policy.entropy(q)
- entropy = policy.entropy(p)
- mean_kl, cov_kl = gaussian_kl(policy, p, q)
- kl = mean_kl + cov_kl
-
- mean_diff, cov_diff = self.trust_region_value(policy, p, q)
-
- combined_constraint = mean_diff + cov_diff
- entropy_diff = entropy_old - entropy
-
- return {'kl': kl.detach().mean(),
- 'constraint': combined_constraint.mean(),
- 'mean_constraint': mean_diff.mean(),
- 'cov_constraint': cov_diff.mean(),
- 'entropy': entropy.mean(),
- 'entropy_diff': entropy_diff.mean(),
- 'kl_max': kl.max(),
- 'constraint_max': combined_constraint.max(),
- 'mean_constraint_max': mean_diff.max(),
- 'cov_constraint_max': cov_diff.max(),
- 'entropy_max': entropy.max(),
- 'entropy_diff_max': entropy_diff.max()
- }
-
- def trust_region_regression(self, policy: AbstractGaussianPolicy, obs: ch.Tensor, q: Tuple[ch.Tensor, ch.Tensor],
- n_minibatches: int, global_steps: int):
- """
- Take additional regression steps to match projection output and policy output.
- The policy parameters are updated in-place.
- Args:
- policy: policy instance
- obs: collected observations from trajectories
- q: old distributions
- n_minibatches: split the rollouts into n_minibatches.
- global_steps: current number of steps, required for projection
- Returns:
- dict with mean of regession loss
- """
-
- if not self.do_regression:
- return {}
-
- policy_unprojected = copy.deepcopy(policy)
- optim_reg = get_optimizer(self.optimizer_type_reg, policy_unprojected.parameters(), learning_rate=self.lr_reg)
- optim_reg.reset()
-
- reg_losses = obs.new_tensor(0.)
-
- # get current projected values --> targets for regression
- p_flat = policy(obs)
- p_target = self(policy, p_flat, q, global_steps)
-
- for _ in range(self.regression_iters):
- batch_indices = generate_minibatches(obs.shape[0], n_minibatches)
-
- # Minibatches SGD
- for indices in batch_indices:
- batch = select_batch(indices, obs, p_target[0], p_target[1])
- b_obs, b_target_mean, b_target_std = batch
- proj_p = (b_target_mean.detach(), b_target_std.detach())
-
- p = policy_unprojected(b_obs)
-
- # invert scaling with coeff here as we do not have to balance with other losses
- loss = self.get_trust_region_loss(policy, p, proj_p) / self.trust_region_coeff
-
- optim_reg.zero_grad()
- loss.backward()
- optim_reg.step()
- reg_losses += loss.detach()
-
- policy.load_state_dict(policy_unprojected.state_dict())
-
- if not policy.contextual_std:
- # set policy with projection value.
- # In non-contextual cases we have only one cov, so the projection is the same.
- policy.set_std(p_target[1][0])
-
- steps = self.regression_iters * (math.ceil(obs.shape[0] / n_minibatches))
- return {"regression_loss": (reg_losses / steps).detach()}
diff --git a/metastable_baselines/projections_orig/frob_projection_layer.py b/metastable_baselines/projections_orig/frob_projection_layer.py
deleted file mode 100644
index 8d338ce..0000000
--- a/metastable_baselines/projections_orig/frob_projection_layer.py
+++ /dev/null
@@ -1,97 +0,0 @@
-# Copyright (c) 2021 Robert Bosch GmbH
-# Author: Fabian Otto
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Affero General Public License as published
-# by the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Affero General Public License for more details.
-#
-# You should have received a copy of the GNU Affero General Public License
-# along with this program. If not, see .
-
-import torch as ch
-from typing import Tuple
-
-from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
-from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer, mean_projection
-from trust_region_projections.utils.projection_utils import gaussian_frobenius
-
-
-class FrobeniusProjectionLayer(BaseProjectionLayer):
-
- def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
- """
- Runs Frobenius projection layer and constructs cholesky of covariance
-
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- eps: (modified) kl bound/ kl bound for mean part
- eps_cov: (modified) kl bound for cov part
- beta: (modified) entropy bound
- **kwargs:
- Returns: mean, cov cholesky
- """
-
- mean, chol = p
- old_mean, old_chol = q
- batch_shape = mean.shape[:-1]
-
- ####################################################################################################################
- # precompute mean and cov part of frob projection, which are used for the projection.
- mean_part, cov_part, cov, cov_old = gaussian_frobenius(policy, p, q, self.scale_prec, True)
-
- ################################################################################################################
- # mean projection maha/euclidean
-
- proj_mean = mean_projection(mean, old_mean, mean_part, eps)
-
- ################################################################################################################
- # cov projection frobenius
-
- cov_mask = cov_part > eps_cov
-
- if cov_mask.any():
- # alpha = ch.where(fro_norm_sq > eps_cov, ch.sqrt(fro_norm_sq / eps_cov) - 1., ch.tensor(1.))
- eta = ch.ones(batch_shape, dtype=chol.dtype, device=chol.device)
- eta[cov_mask] = ch.sqrt(cov_part[cov_mask] / eps_cov) - 1.
- eta = ch.max(-eta, eta)
-
- new_cov = (cov + ch.einsum('i,ijk->ijk', eta, cov_old)) / (1. + eta + 1e-16)[..., None, None]
- proj_chol = ch.where(cov_mask[..., None, None], ch.cholesky(new_cov), chol)
- else:
- proj_chol = chol
-
- return proj_mean, proj_chol
-
- def trust_region_value(self, policy, p, q):
- """
- Computes the Frobenius metric between two Gaussian distributions p and q.
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- Returns:
- mean and covariance part of Frobenius metric
- """
- return gaussian_frobenius(policy, p, q, self.scale_prec)
-
- def get_trust_region_loss(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- proj_p: Tuple[ch.Tensor, ch.Tensor]):
-
- mean_diff, _ = self.trust_region_value(policy, p, proj_p)
- if policy.contextual_std:
- # Compute MSE here, because we found the Frobenius norm tends to generate values that explode for the cov
- cov_diff = (p[1] - proj_p[1]).pow(2).sum([-1, -2])
- delta_loss = (mean_diff + cov_diff).mean()
- else:
- delta_loss = mean_diff.mean()
-
- return delta_loss * self.trust_region_coeff
diff --git a/metastable_baselines/projections_orig/kl_projection_layer.py b/metastable_baselines/projections_orig/kl_projection_layer.py
deleted file mode 100644
index ca5acd5..0000000
--- a/metastable_baselines/projections_orig/kl_projection_layer.py
+++ /dev/null
@@ -1,101 +0,0 @@
-import cpp_projection
-import numpy as np
-import torch as ch
-from typing import Any, Tuple
-
-from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
-from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer, mean_projection
-from trust_region_projections.utils.projection_utils import gaussian_kl
-from trust_region_projections.utils.torch_utils import get_numpy
-
-
-class KLProjectionLayer(BaseProjectionLayer):
-
- def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
- """
- Runs KL projection layer and constructs cholesky of covariance
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- eps: (modified) kl bound/ kl bound for mean part
- eps_cov: (modified) kl bound for cov part
- **kwargs:
-
- Returns:
- projected mean, projected cov cholesky
- """
- mean, std = p
- old_mean, old_std = q
-
- if not policy.contextual_std:
- # only project first one to reduce number of numerical optimizations
- std = std[:1]
- old_std = old_std[:1]
-
- ################################################################################################################
- # project mean with closed form
- mean_part, _ = gaussian_kl(policy, p, q)
- proj_mean = mean_projection(mean, old_mean, mean_part, eps)
-
- cov = policy.covariance(std)
- old_cov = policy.covariance(old_std)
-
- if policy.is_diag:
- proj_cov = KLProjectionGradFunctionDiagCovOnly.apply(cov.diagonal(dim1=-2, dim2=-1),
- old_cov.diagonal(dim1=-2, dim2=-1),
- eps_cov)
- proj_std = proj_cov.sqrt().diag_embed()
- else:
- raise NotImplementedError("The KL projection currently does not support full covariance matrices.")
-
- if not policy.contextual_std:
- # scale first std back to batchsize
- proj_std = proj_std.expand(mean.shape[0], -1, -1)
-
- return proj_mean, proj_std
-
-
-class KLProjectionGradFunctionDiagCovOnly(ch.autograd.Function):
- projection_op = None
-
- @staticmethod
- def get_projection_op(batch_shape, dim, max_eval=100):
- if not KLProjectionGradFunctionDiagCovOnly.projection_op:
- KLProjectionGradFunctionDiagCovOnly.projection_op = \
- cpp_projection.BatchedDiagCovOnlyProjection(batch_shape, dim, max_eval=max_eval)
- return KLProjectionGradFunctionDiagCovOnly.projection_op
-
- @staticmethod
- def forward(ctx: Any, *args: Any, **kwargs: Any) -> Any:
- std, old_std, eps_cov = args
-
- batch_shape = std.shape[0]
- dim = std.shape[-1]
-
- cov_np = get_numpy(std)
- old_std = get_numpy(old_std)
- eps = get_numpy(eps_cov) * np.ones(batch_shape)
-
- # p_op = cpp_projection.BatchedDiagCovOnlyProjection(batch_shape, dim)
- # ctx.proj = projection_op
-
- p_op = KLProjectionGradFunctionDiagCovOnly.get_projection_op(batch_shape, dim)
- ctx.proj = p_op
-
- proj_std = p_op.forward(eps, old_std, cov_np)
-
- return std.new(proj_std)
-
- @staticmethod
- def backward(ctx: Any, *grad_outputs: Any) -> Any:
- projection_op = ctx.proj
- d_std, = grad_outputs
-
- d_std_np = get_numpy(d_std)
- d_std_np = np.atleast_2d(d_std_np)
- df_stds = projection_op.backward(d_std_np)
- df_stds = np.atleast_2d(df_stds)
-
- return d_std.new(df_stds), None, None
diff --git a/metastable_baselines/projections_orig/papi_projection.py b/metastable_baselines/projections_orig/papi_projection.py
deleted file mode 100644
index b52db75..0000000
--- a/metastable_baselines/projections_orig/papi_projection.py
+++ /dev/null
@@ -1,233 +0,0 @@
-# Copyright (c) 2021 Robert Bosch GmbH
-# Author: Fabian Otto
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Affero General Public License as published
-# by the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Affero General Public License for more details.
-#
-# You should have received a copy of the GNU Affero General Public License
-# along with this program. If not, see .
-
-import logging
-
-import copy
-import numpy as np
-import torch as ch
-from typing import Tuple, Union
-
-from trust_region_projections.utils.projection_utils import gaussian_kl
-from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
-from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer
-from trust_region_projections.utils.torch_utils import torch_batched_trace
-
-logger = logging.getLogger("papi_projection")
-
-
-class PAPIProjection(BaseProjectionLayer):
-
- def __init__(self,
- proj_type: str = "",
- mean_bound: float = 0.015,
- cov_bound: float = 0.0,
-
- entropy_eq: bool = False,
- entropy_first: bool = True,
-
- cpu: bool = True,
- dtype: ch.dtype = ch.float32,
- **kwargs
- ):
-
- """
- PAPI projection, which can be used after each training epoch to satisfy the trust regions.
- Args:
- proj_type: Which type of projection to use. None specifies no projection and uses the TRPO objective.
- mean_bound: projection bound for the step size,
- PAPI only has a joint KL constraint, mean and cov bound are summed for this bound.
- cov_bound: projection bound for the step size,
- PAPI only has a joint KL constraint, mean and cov bound are summed for this bound.
- entropy_eq: Use entropy equality constraints.
- entropy_first: Project entropy before trust region.
- cpu: Compute on CPU only.
- dtype: Data type to use, either of float32 or float64. The later might be necessary for higher
- dimensions in order to learn the full covariance.
- """
-
- assert entropy_first
- super().__init__(proj_type, mean_bound, cov_bound, 0.0, False, None, None, None, 0.0, 0.0, entropy_eq,
- entropy_first, cpu, dtype)
-
- self.last_policies = []
-
- def __call__(self, policy, p, q, step=0, *args, **kwargs):
- if kwargs.get("obs"):
- self._papi_steps(policy, q, **kwargs)
- else:
- return p
-
- def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- q: Tuple[ch.Tensor, ch.Tensor], eps: Union[ch.Tensor, float],
- eps_cov: Union[ch.Tensor, float], **kwargs):
- """
- runs papi projection layer and constructs sqrt of covariance
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- eps: (modified) kl bound/ kl bound for mean part
- eps_cov: (modified) kl bound for cov part
- **kwargs:
-
- Returns:
- mean, cov sqrt
- """
-
- mean, chol = p
- old_mean, old_chol = q
- intermed_mean = kwargs.get('intermed_mean')
-
- dtype = mean.dtype
- device = mean.device
-
- dim = mean.shape[-1]
-
- ################################################################################################################
- # Precompute basic matrices
-
- # Joint bound
- eps += eps_cov
-
- I = ch.eye(dim, dtype=dtype, device=device)
- old_precision = ch.cholesky_solve(I, old_chol)[0]
- logdet_old = policy.log_determinant(old_chol)
- cov = policy.covariance(chol)
-
- ################################################################################################################
- # compute expected KL
- maha_part, cov_part = gaussian_kl(policy, p, q)
- maha_part = maha_part.mean()
- cov_part = cov_part.mean()
-
- if intermed_mean is not None:
- maha_intermediate = 0.5 * policy.maha(intermed_mean, old_mean, old_chol).mean()
- mm = ch.min(maha_part, maha_intermediate)
-
- ################################################################################################################
- # matrix rotation/rescaling projection
- if maha_part + cov_part > eps + 1e-6:
- old_cov = policy.covariance(old_chol)
-
- maha_delta = eps if intermed_mean is None else (eps - mm)
- eta_rot = maha_delta / ch.max(maha_part + cov_part, ch.tensor(1e-16, dtype=dtype, device=device))
- new_cov = (1 - eta_rot) * old_cov + eta_rot * cov
- proj_chol = ch.cholesky(new_cov)
-
- # recompute covariance part of KL for new chol
- trace_term = 0.5 * (torch_batched_trace(old_precision @ new_cov) - dim).mean() # rotation difference
- entropy_diff = 0.5 * (logdet_old - policy.log_determinant(proj_chol)).mean()
-
- cov_part = trace_term + entropy_diff
-
- else:
- proj_chol = chol
-
- ################################################################################################################
- # mean interpolation projection
- if maha_part + cov_part > eps + 1e-6:
-
- if intermed_mean is not None:
- a = 0.5 * policy.maha(mean, intermed_mean, old_chol).mean()
- b = 0.5 * ((mean - intermed_mean) @ old_precision @ (intermed_mean - old_mean).T).mean()
- c = maha_intermediate - ch.max(eps - cov_part, ch.tensor(0., dtype=dtype, device=device))
- eta_mean = (-b + ch.sqrt(ch.max(b * b - a * c, ch.tensor(1e-16, dtype=dtype, device=device)))) / \
- ch.max(a, ch.tensor(1e-16, dtype=dtype, device=device))
- else:
- eta_mean = ch.sqrt(
- ch.max(eps - cov_part, ch.tensor(1e-16, dtype=dtype, device=device)) /
- ch.max(maha_part, ch.tensor(1e-16, dtype=dtype, device=device)))
- else:
- eta_mean = ch.tensor(1., dtype=dtype, device=device)
-
- return eta_mean, proj_chol
-
- def _papi_steps(self, policy: AbstractGaussianPolicy, q: Tuple[ch.Tensor, ch.Tensor], obs: ch.Tensor, lr_schedule,
- lr_schedule_vf=None):
- """
- Take PAPI steps after PPO finished its steps. Policy parameters are updated in-place.
- Args:
- policy: policy instance
- q: old distribution
- obs: collected observations from trajectories
- lr_schedule: lr schedule for policy
- lr_schedule_vf: lr schedule for vf
-
- Returns:
-
- """
- assert not policy.contextual_std
-
- # save latest policy in history
- self.last_policies.append(copy.deepcopy(policy))
-
- ################################################################################################################
- # policy backtracking: out of last n policies and current one find one that satisfies the kl constraint
-
- intermed_policy = None
- n_backtracks = 0
-
- for i, pi in enumerate(reversed(self.last_policies)):
- p_prime = pi(obs)
- mean_part, cov_part = pi.kl_divergence(p_prime, q)
- if (mean_part + cov_part).mean() <= self.mean_bound + self.cov_bound:
- intermed_policy = pi
- n_backtracks = i
- break
-
- ################################################################################################################
- # LR update
-
- # reduce learning rate when appropriate policy not within the last 4 epochs
- if n_backtracks >= 4 or intermed_policy is None:
- # Linear learning rate annealing
- lr_schedule.step()
- if lr_schedule_vf:
- lr_schedule_vf.step()
-
- if intermed_policy is None:
- # pop last policy and make it current one, as the updated one was poor
- # do not keep last policy in history, otherwise we could stack the same policy multiple times.
- if len(self.last_policies) >= 1:
- policy.load_state_dict(self.last_policies.pop().state_dict())
- logger.warning(f"No suitable policy found in backtracking of {len(self.last_policies)} policies.")
- return
-
- ################################################################################################################
- # PAPI iterations
-
- # We assume only non contextual covariances here, therefore we only need to project for one
- q = (q[0], q[1][:1]) # (means, covs[:1])
-
- # This is A from Alg. 2 [Akrour et al., 2019]
- intermed_weight = intermed_policy.get_last_layer().detach().clone()
- # This is A @ phi(s)
- intermed_mean = p_prime[0].detach().clone()
-
- entropy = policy.entropy(q)
- entropy_bound = obs.new_tensor([-np.inf]) if entropy / self.initial_entropy > 0.5 \
- else entropy - (self.mean_bound + self.cov_bound)
-
- for _ in range(20):
- eta, proj_chol = self._projection(intermed_policy, (p_prime[0], p_prime[1][:1]), q,
- self.mean_bound, self.cov_bound, entropy_bound,
- intermed_mean=intermed_mean)
- intermed_policy.papi_weight_update(eta, intermed_weight)
- intermed_policy.set_std(proj_chol[0])
- p_prime = intermed_policy(obs)
-
- policy.load_state_dict(intermed_policy.state_dict())
diff --git a/metastable_baselines/projections_orig/projection_factory.py b/metastable_baselines/projections_orig/projection_factory.py
deleted file mode 100644
index 9c38275..0000000
--- a/metastable_baselines/projections_orig/projection_factory.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# Copyright (c) 2021 Robert Bosch GmbH
-# Author: Fabian Otto
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Affero General Public License as published
-# by the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Affero General Public License for more details.
-#
-# You should have received a copy of the GNU Affero General Public License
-# along with this program. If not, see .
-
-from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer
-from trust_region_projections.projections.frob_projection_layer import FrobeniusProjectionLayer
-from trust_region_projections.projections.kl_projection_layer import KLProjectionLayer
-from trust_region_projections.projections.papi_projection import PAPIProjection
-from trust_region_projections.projections.w2_projection_layer import WassersteinProjectionLayer
-
-
-def get_projection_layer(proj_type: str = "", **kwargs) -> BaseProjectionLayer:
- """
- Factory to generate the projection layers for all projections.
- Args:
- proj_type: One of None/' ', 'ppo', 'papi', 'w2', 'w2_non_com', 'frob', 'kl', or 'entropy'
- **kwargs: arguments for projection layer
-
- Returns:
-
- """
- if not proj_type or proj_type.isspace() or proj_type.lower() in ["ppo", "sac", "td3", "mpo", "entropy"]:
- return BaseProjectionLayer(proj_type, **kwargs)
-
- elif proj_type.lower() == "w2":
- return WassersteinProjectionLayer(proj_type, **kwargs)
-
- elif proj_type.lower() == "frob":
- return FrobeniusProjectionLayer(proj_type, **kwargs)
-
- elif proj_type.lower() == "kl":
- return KLProjectionLayer(proj_type, **kwargs)
-
- elif proj_type.lower() == "papi":
- # papi has a different approach compared to our projections.
- # It has to be applied after the training with PPO.
- return PAPIProjection(proj_type, **kwargs)
-
- else:
- raise ValueError(
- f"Invalid projection type {proj_type}."
- f" Choose one of None/' ', 'ppo', 'papi', 'w2', 'w2_non_com', 'frob', 'kl', or 'entropy'.")
diff --git a/metastable_baselines/projections_orig/w2_projection_layer.py b/metastable_baselines/projections_orig/w2_projection_layer.py
deleted file mode 100644
index bce87a3..0000000
--- a/metastable_baselines/projections_orig/w2_projection_layer.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Copyright (c) 2021 Robert Bosch GmbH
-# Author: Fabian Otto
-#
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU Affero General Public License as published
-# by the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
-#
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
-# GNU Affero General Public License for more details.
-#
-# You should have received a copy of the GNU Affero General Public License
-# along with this program. If not, see .
-
-import torch as ch
-from typing import Tuple
-
-from trust_region_projections.models.policy.abstract_gaussian_policy import AbstractGaussianPolicy
-from trust_region_projections.projections.base_projection_layer import BaseProjectionLayer, mean_projection
-from trust_region_projections.utils.projection_utils import gaussian_wasserstein_commutative
-
-
-class WassersteinProjectionLayer(BaseProjectionLayer):
-
- def _trust_region_projection(self, policy: AbstractGaussianPolicy, p: Tuple[ch.Tensor, ch.Tensor],
- q: Tuple[ch.Tensor, ch.Tensor], eps: ch.Tensor, eps_cov: ch.Tensor, **kwargs):
- """
- Runs commutative Wasserstein projection layer and constructs sqrt of covariance
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- eps: (modified) kl bound/ kl bound for mean part
- eps_cov: (modified) kl bound for cov part
- **kwargs:
-
- Returns:
- mean, cov sqrt
- """
- mean, sqrt = p
- old_mean, old_sqrt = q
- batch_shape = mean.shape[:-1]
-
- ####################################################################################################################
- # precompute mean and cov part of W2, which are used for the projection.
- # Both parts differ based on precision scaling.
- # If activated, the mean part is the maha distance and the cov has a more complex term in the inner parenthesis.
- mean_part, cov_part = gaussian_wasserstein_commutative(policy, p, q, self.scale_prec)
-
- ####################################################################################################################
- # project mean (w/ or w/o precision scaling)
- proj_mean = mean_projection(mean, old_mean, mean_part, eps)
-
- ####################################################################################################################
- # project covariance (w/ or w/o precision scaling)
-
- cov_mask = cov_part > eps_cov
-
- if cov_mask.any():
- # gradient issue with ch.where, it executes both paths and gives NaN gradient.
- eta = ch.ones(batch_shape, dtype=sqrt.dtype, device=sqrt.device)
- eta[cov_mask] = ch.sqrt(cov_part[cov_mask] / eps_cov) - 1.
- eta = ch.max(-eta, eta)
-
- new_sqrt = (sqrt + ch.einsum('i,ijk->ijk', eta, old_sqrt)) / (1. + eta + 1e-16)[..., None, None]
- proj_sqrt = ch.where(cov_mask[..., None, None], new_sqrt, sqrt)
- else:
- proj_sqrt = sqrt
-
- return proj_mean, proj_sqrt
-
- def trust_region_value(self, policy, p, q):
- """
- Computes the Wasserstein distance between two Gaussian distributions p and q.
- Args:
- policy: policy instance
- p: current distribution
- q: old distribution
- Returns:
- mean and covariance part of Wasserstein distance
- """
- return gaussian_wasserstein_commutative(policy, p, q, scale_prec=self.scale_prec)
\ No newline at end of file