Making MultivariateNormal Policies work (and porting Normal to

Independent)

metastable_baselines/distributions/distributions.py

@@ -4,7 +4,7 @@ from enum import Enum
 import gym
 import torch as th
 from torch import nn
-from torch.distributions import Normal, MultivariateNormal
+from torch.distributions import Normal, Independent, MultivariateNormal
 from math import pi
 
 from stable_baselines3.common.preprocessing import get_action_dim
@@ -37,6 +37,7 @@ class Strength(Enum):
 
 
 class ParametrizationType(Enum):
+    NONE = 0
     CHOL = 1
     SPHERICAL_CHOL = 2
     # Not (yet?) implemented:
@@ -46,6 +47,7 @@ class ParametrizationType(Enum):
 
 class EnforcePositiveType(Enum):
     # TODO: Allow custom params for softplus?
+    NONE = (0, nn.Identity())
     SOFTPLUS = (1, nn.Softplus(beta=1, threshold=20))
     ABS = (2, th.abs)
     RELU = (3, nn.ReLU(inplace=False))
@@ -89,14 +91,14 @@ def get_legal_setups(allowedEPTs=None, allowedParStrength=None, allowedCovStreng
                 # TODO: Implement
                 continue
             if ps == Strength.NONE:
-                yield (ps, cs, None, None)
+                yield (ps, cs, EnforcePositiveType.NONE, ProbSquashingType.NONE)
             else:
                 for ept in allowedEPTs:
                     if cs == Strength.FULL:
                         for pt in allowedPTs:
                             yield (ps, cs, ept, pt)
                     else:
-                        yield (ps, cs, ept, None)
+                        yield (ps, cs, ept, ProbSquashingType.NONE)
 
 
 def make_proba_distribution(
@@ -138,7 +140,7 @@ class UniversalGaussianDistribution(SB3_Distribution):
     :param action_dim:  Dimension of the action space.
     """
 
-    def __init__(self, action_dim: int, use_sde: bool = False, neural_strength: Strength = Strength.DIAG, cov_strength: Strength = Strength.DIAG, parameterization_type: ParametrizationType = ParametrizationType.CHOL, enforce_positive_type: EnforcePositiveType = EnforcePositiveType.ABS, prob_squashing_type: ProbSquashingType = ProbSquashingType.NONE):
+    def __init__(self, action_dim: int, use_sde: bool = False, neural_strength: Strength = Strength.DIAG, cov_strength: Strength = Strength.DIAG, parameterization_type: ParametrizationType = ParametrizationType.NONE, enforce_positive_type: EnforcePositiveType = EnforcePositiveType.ABS, prob_squashing_type: ProbSquashingType = ProbSquashingType.NONE):
         super(UniversalGaussianDistribution, self).__init__()
         self.action_dim = action_dim
         self.par_strength = neural_strength
@@ -155,16 +157,19 @@ class UniversalGaussianDistribution(SB3_Distribution):
         if use_sde:
             raise Exception('SDE is not yet implemented')
 
+        assert (parameterization_type != ParametrizationType.NONE) == (
+            cov_strength == Strength.FULL), 'You should set an ParameterizationType iff the cov-strength is full'
+
     def new_dist_like_me(self, mean: th.Tensor, chol: th.Tensor):
         p = self.distribution
-        if isinstance(p, th.distributions.Normal):
+        if isinstance(p, Independent):
             if p.stddev.shape != chol.shape:
                 chol = th.diagonal(chol, dim1=1, dim2=2)
-            np = th.distributions.Normal(mean, chol)
+            np = Independent(Normal(mean, chol), 1)
-        elif isinstance(p, th.distributions.MultivariateNormal):
+        elif isinstance(p, MultivariateNormal):
-            np = th.distributions.MultivariateNormal(mean, scale_tril=chol)
+            np = MultivariateNormal(mean, scale_tril=chol)
         new = UniversalGaussianDistribution(self.action_dim, neural_strength=self.par_strength, cov_strength=self.cov_strength,
-                                            parameterization_type=self.par_strength, enforce_positive_type=self.enforce_positive_type, prob_squashing_type=self.prob_squashing_type)
+                                            parameterization_type=self.par_type, enforce_positive_type=self.enforce_positive_type, prob_squashing_type=self.prob_squashing_type)
         new.distribution = np
 
         return new
@@ -202,9 +207,10 @@ class UniversalGaussianDistribution(SB3_Distribution):
         # TODO: latent_pi is for SDE, implement.
 
         if self.cov_strength in [Strength.NONE, Strength.SCALAR, Strength.DIAG]:
-            self.distribution = Normal(mean_actions, chol)
+            self.distribution = Independent(Normal(mean_actions, chol), 1)
         elif self.cov_strength in [Strength.FULL]:
-            self.distribution = MultivariateNormal(mean_actions, cholesky=chol)
+            self.distribution = MultivariateNormal(
+                mean_actions, scale_tril=chol)
         if self.distribution == None:
             raise Exception('Unable to create torch distribution')
         return self
@@ -218,10 +224,10 @@ class UniversalGaussianDistribution(SB3_Distribution):
         :return:
         """
         log_prob = self.distribution.log_prob(actions)
-        return sum_independent_dims(log_prob)
+        return log_prob
 
     def entropy(self) -> th.Tensor:
-        return sum_independent_dims(self.distribution.entropy())
+        return self.distribution.entropy()
 
     def sample(self) -> th.Tensor:
         # Reparametrization trick to pass gradients

metastable_baselines/misc/distTools.py

@@ -6,7 +6,7 @@ from ..distributions import UniversalGaussianDistribution, AnyDistribution
 
 
 def get_mean_and_chol(p: AnyDistribution, expand=False):
-    if isinstance(p, th.distributions.Normal):
+    if isinstance(p, th.distributions.Normal) or isinstance(p, th.distributions.Independent):
         if expand:
             return p.mean, th.diag_embed(p.stddev)
         else:
@@ -32,7 +32,7 @@ def get_mean_and_sqrt(p: UniversalGaussianDistribution):
 
 
 def get_cov(p: AnyDistribution):
-    if isinstance(p, th.distributions.Normal):
+    if isinstance(p, th.distributions.Normal) or isinstance(p, th.distributions.Independent):
         return th.diag_embed(p.variance)
     elif isinstance(p, th.distributions.MultivariateNormal):
         return p.covariance_matrix
@@ -45,7 +45,7 @@ def get_cov(p: AnyDistribution):
 def has_diag_cov(p: AnyDistribution, numerical_check=True):
     if isinstance(p, SB3_Distribution):
         return has_diag_cov(p.distribution, numerical_check=numerical_check)
-    if isinstance(p, th.distributions.Normal):
+    if isinstance(p, th.distributions.Normal) or isinstance(p, th.distributions.Independent):
         return True
     if not numerical_check:
         return False
@@ -67,11 +67,15 @@ def get_diag_cov_vec(p: AnyDistribution, check_diag=True, numerical_check=True):
 
 def new_dist_like(orig_p: AnyDistribution, mean: th.Tensor, chol: th.Tensor):
     if isinstance(orig_p, UniversalGaussianDistribution):
-        return orig_p.new_list_like_me(mean, chol)
+        return orig_p.new_dist_like_me(mean, chol)
     elif isinstance(orig_p, th.distributions.Normal):
         if orig_p.stddev.shape != chol.shape:
             chol = th.diagonal(chol, dim1=1, dim2=2)
         return th.distributions.Normal(mean, chol)
+    elif isinstance(orig_p, th.distributions.Independent):
+        if orig_p.stddev.shape != chol.shape:
+            chol = th.diagonal(chol, dim1=1, dim2=2)
+        return th.distributions.Independent(th.distributions.Normal(mean, chol), 1)
     elif isinstance(orig_p, th.distributions.MultivariateNormal):
         return th.distributions.MultivariateNormal(mean, scale_tril=chol)
     elif isinstance(orig_p, SB3_Distribution):
@@ -79,6 +83,10 @@ def new_dist_like(orig_p: AnyDistribution, mean: th.Tensor, chol: th.Tensor):
         if isinstance(p, th.distributions.Normal):
             p_out = orig_p.__class__(orig_p.action_dim)
             p_out.distribution = th.distributions.Normal(mean, chol)
+        elif isinstance(p, th.distributions.Independent):
+            p_out = orig_p.__class__(orig_p.action_dim)
+            p_out.distribution = th.distributions.Independent(
+                th.distributions.Normal(mean, chol), 1)
         elif isinstance(p, th.distributions.MultivariateNormal):
             p_out = orig_p.__class__(orig_p.action_dim)
             p_out.distribution = th.distributions.MultivariateNormal(

metastable_baselines/misc/rollout_buffer.py

@@ -1,4 +1,5 @@
 from typing import Any, Dict, Optional, Type, Union, NamedTuple
+from more_itertools import distribute
 
 import numpy as np
 import torch as th
@@ -10,6 +11,8 @@ from stable_baselines3.common.vec_env import VecEnv
 from stable_baselines3.common.callbacks import BaseCallback
 from stable_baselines3.common.utils import obs_as_tensor
 
+from ..misc.distTools import get_mean_and_chol
+from ..distributions.distributions import Strength, UniversalGaussianDistribution
 
 # TRL requires the origina mean and covariance from the policy when the datapoint was created.
 # GaussianRolloutBuffer extends the RolloutBuffer by these two fields
@@ -120,6 +123,12 @@ class GaussianRolloutCollectorAuxclass():
     def _setup_model(self) -> None:
         super()._setup_model()
 
+        cov_shape = self.action_space.shape
+
+        if isinstance(self.policy.action_dist, UniversalGaussianDistribution):
+            if self.policy.action_dist.cov_strength == Strength.FULL:
+                cov_shape = cov_shape + cov_shape
+
         self.rollout_buffer = GaussianRolloutBuffer(
             self.n_steps,
             self.observation_space,
@@ -128,6 +137,7 @@ class GaussianRolloutCollectorAuxclass():
             gamma=self.gamma,
             gae_lambda=self.gae_lambda,
             n_envs=self.n_envs,
+            cov_shape=cov_shape,
         )
 
     def collect_rollouts(

metastable_baselines/ppo/__init__.py

@@ -1,2 +1,2 @@
-from ..trl_pg.policies import CnnPolicy, MlpPolicy, MultiInputPolicy
+from ..ppo.policies import CnnPolicy, MlpPolicy, MultiInputPolicy
-from ..trl_pg.trl_pg import TRL_PG
+from .ppo import PPO

metastable_baselines/ppo/policies.py

@@ -95,6 +95,7 @@ class ActorCriticPolicy(BasePolicy):
         normalize_images: bool = True,
         optimizer_class: Type[th.optim.Optimizer] = th.optim.Adam,
         optimizer_kwargs: Optional[Dict[str, Any]] = None,
+        dist_kwargs: Optional[Dict[str, Any]] = None,
     ):
 
         if optimizer_kwargs is None:
@@ -130,15 +131,16 @@ class ActorCriticPolicy(BasePolicy):
 
         self.normalize_images = normalize_images
         self.log_std_init = log_std_init
-        dist_kwargs = None
         # Keyword arguments for gSDE distribution
         if use_sde:
-            dist_kwargs = {
+            add_dist_kwargs = {
                 "full_std": full_std,
                 "squash_output": squash_output,
                 "use_expln": use_expln,
                 "learn_features": False,
             }
+            for k in add_dist_kwargs:
+                dist_kwargs[k] = add_dist_kwargs[k]
 
         if sde_net_arch is not None:
             warnings.warn(

metastable_baselines/ppo/ppo.py

@@ -26,7 +26,7 @@ from ..projections.kl_projection_layer import KLProjectionLayer
 from ..misc.rollout_buffer import GaussianRolloutCollectorAuxclass
 
 
-class TRL_PG(GaussianRolloutCollectorAuxclass, OnPolicyAlgorithm):
+class PPO(GaussianRolloutCollectorAuxclass, OnPolicyAlgorithm):
     """
     Differential Trust Region Layer (TRL) for Policy Gradient (PG)
 
@@ -248,7 +248,12 @@ class TRL_PG(GaussianRolloutCollectorAuxclass, OnPolicyAlgorithm):
                 q_dist = new_dist_like(
                     p_dist, rollout_data.means, rollout_data.stds)
                 proj_p = self.projection(p_dist, q_dist, self._global_steps)
+                if isinstance(p_dist, th.distributions.Normal):
+                    # Normal uses a weird mapping from dimensions into batch_shape
                     log_prob = proj_p.log_prob(actions).sum(dim=1)
+                else:
+                    # UniversalGaussianDistribution instead uses Independent (or MultivariateNormal), which has a more rational dim mapping
+                    log_prob = proj_p.log_prob(actions)
                 values = self.policy.value_net(latent_vf)
                 entropy = proj_p.entropy()
 
@@ -373,10 +378,10 @@ class TRL_PG(GaussianRolloutCollectorAuxclass, OnPolicyAlgorithm):
         eval_env: Optional[GymEnv] = None,
         eval_freq: int = -1,
         n_eval_episodes: int = 5,
-        tb_log_name: str = "TRL_PG",
+        tb_log_name: str = "PPO",
         eval_log_path: Optional[str] = None,
         reset_num_timesteps: bool = True,
-    ) -> "TRL_PG":
+    ) -> "PPO":
 
         return super().learn(
             total_timesteps=total_timesteps,

replay.py

@@ -6,11 +6,10 @@ import os
 import time
 import datetime
 
-from stable_baselines3 import SAC, PPO, A2C
 from stable_baselines3.common.evaluation import evaluate_policy
 from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, MultiInputActorCriticPolicy
 
-from metastable_baselines.trl_pg import TRL_PG
+from metastable_baselines.ppo import PPO
 import columbus
 
 
@@ -26,14 +25,8 @@ def main(load_path, n_eval_episodes=0):
     use_sde = file_name.find('sde') != -1
     print(env_name, alg_name, alg_deriv, use_sde)
     env = gym.make(env_name)
-    if alg_name == 'ppo':
-        Model = PPO
-    elif alg_name == 'trl' and alg_deriv == 'pg':
-        Model = TRL_PG
-    else:
-        raise Exception('Algorithm not implemented for replay')
 
-    model = Model.load(load_path, env=env)
+    model = PPO.load(load_path, env=env)
 
     if n_eval_episodes:
         mean_reward, std_reward = evaluate_policy(

test.py

@@ -6,25 +6,28 @@ import os
 import time
 import datetime
 
-from stable_baselines3 import
 from stable_baselines3.common.evaluation import evaluate_policy
 from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, MultiInputActorCriticPolicy
 
 from metastable_baselines.ppo import PPO
+# from metastable_baselines.sac import SAC
 from metastable_baselines.ppo.policies import MlpPolicy
 from metastable_baselines.projections import BaseProjectionLayer, FrobeniusProjectionLayer, WassersteinProjectionLayer, KLProjectionLayer
 import columbus
 
-#root_path = os.getcwd()
+from metastable_baselines.distributions import Strength, ParametrizationType, EnforcePositiveType, ProbSquashingType
+
 root_path = '.'
 
 
-def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=10_000_000, showRes=True, saveModel=True, n_eval_episodes=0):
+def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=2_000_000, showRes=True, saveModel=True, n_eval_episodes=0):
     env = gym.make(env_name)
     use_sde = False
     ppo = PPO(
         MlpPolicy,
         env,
+        policy_kwargs={'dist_kwargs': {'neural_strength': Strength.DIAG, 'cov_strength': Strength.DIAG, 'parameterization_type':
+                       ParametrizationType.NONE, 'enforce_positive_type': EnforcePositiveType.ABS, 'prob_squashing_type': ProbSquashingType.NONE}},
         verbose=0,
         tensorboard_log=root_path+"/logs_tb/" +
         env_name+"/ppo"+(['', '_sde'][use_sde])+"/",
@@ -37,29 +40,29 @@ def main(env_name='ColumbusCandyland_Aux10-v0', timesteps=10_000_000, showRes=Tr
         use_sde=use_sde,  # False
         clip_range=0.2,
     )
-    trl_frob = PPO(
+    # trl_frob = PPO(
-        MlpPolicy,
+    #    MlpPolicy,
-        env,
+    #    env,
-        projection=FrobeniusProjectionLayer(),
+    #    projection=FrobeniusProjectionLayer(),
-        verbose=0,
+    #    verbose=0,
-        tensorboard_log=root_path+"/logs_tb/"+env_name +
+    #    tensorboard_log=root_path+"/logs_tb/"+env_name +
-        "/trl_frob"+(['', '_sde'][use_sde])+"/",
+    #    "/trl_frob"+(['', '_sde'][use_sde])+"/",
-        learning_rate=3e-4,
+    #    learning_rate=3e-4,
-        gamma=0.99,
+    #    gamma=0.99,
-        gae_lambda=0.95,
+    #    gae_lambda=0.95,
-        normalize_advantage=True,
+    #    normalize_advantage=True,
-        ent_coef=0.03,  # 0.1
+    #    ent_coef=0.03,  # 0.1
-        vf_coef=0.5,
+    #    vf_coef=0.5,
-        use_sde=use_sde,
+    #    use_sde=use_sde,
-        clip_range=2,  # 0.2
+    #    clip_range=2,  # 0.2
-    )
+    # )
 
     print('PPO:')
     testModel(ppo, timesteps, showRes,
               saveModel, n_eval_episodes)
-    print('TRL_frob:')
+    # print('TRL_frob:')
-    testModel(trl_frob, timesteps, showRes,
+    # testModel(trl_frob, timesteps, showRes,
-              saveModel, n_eval_episodes)
+    #          saveModel, n_eval_episodes)
 
 
 def testModel(model, timesteps, showRes=False, saveModel=False, n_eval_episodes=16):