Fix entropy computation

docs/misc/changelog.rst

@@ -21,6 +21,7 @@ Bug Fixes:
 ^^^^^^^^^^
 - Synced callbacks with Stable-Baselines
 - Fixed colors in `results_plotter`
+- Fix entropy computation (now summed over action dim)
 
 Deprecations:
 ^^^^^^^^^^^^^
@@ -34,6 +35,7 @@ Others:
 - Add test for ``expln``
 - Renamed ``learning_rate`` to ``lr_schedule``
 - Add ``version.txt``
+- Add more tests for distribution
 
 Documentation:
 ^^^^^^^^^^^^^^

tests/test_distributions.py

@@ -2,18 +2,22 @@ import pytest
 import torch as th
 
 from torchy_baselines import A2C, PPO
-from torchy_baselines.common.distributions import DiagGaussianDistribution, TanhBijector, \
-    StateDependentNoiseDistribution
+from torchy_baselines.common.distributions import (DiagGaussianDistribution, TanhBijector,
+                                                   StateDependentNoiseDistribution,
+                                                   CategoricalDistribution)
 from torchy_baselines.common.utils import set_random_seed
 
 
-# TODO: more tests for the other distributions
+N_ACTIONS = 2
+N_FEATURES = 3
+N_SAMPLES = int(5e6)
+
+
 def test_bijector():
     """
     Test TanhBijector
     """
     actions = th.ones(5) * 2.0
-
     bijector = TanhBijector()
 
     squashed_actions = bijector.forward(actions)
@@ -33,16 +37,14 @@ def test_squashed_gaussian(model_class):
 
 
 def test_sde_distribution():
-    n_samples = int(5e6)
-    n_features = 2
     n_actions = 1
-    deterministic_actions = th.ones(n_samples, n_actions) * 0.1
-    state = th.ones(n_samples, n_features) * 0.3
+    deterministic_actions = th.ones(N_SAMPLES, n_actions) * 0.1
+    state = th.ones(N_SAMPLES, N_FEATURES) * 0.3
     dist = StateDependentNoiseDistribution(n_actions, full_std=True, squash_output=False)
 
     set_random_seed(1)
-    _, log_std = dist.proba_distribution_net(n_features)
-    dist.sample_weights(log_std, batch_size=n_samples)
+    _, log_std = dist.proba_distribution_net(N_FEATURES)
+    dist.sample_weights(log_std, batch_size=N_SAMPLES)
 
     actions, _ = dist.proba_distribution(deterministic_actions, log_std, state)
 
@@ -50,10 +52,6 @@ def test_sde_distribution():
     assert th.allclose(actions.std(), dist.distribution.scale.mean(), rtol=1e-3)
 
 
-N_ACTIONS = 1
-
-
-# TODO: fix for num action > 1
 # TODO: analytical form for squashed Gaussian?
 @pytest.mark.parametrize("dist", [
     DiagGaussianDistribution(N_ACTIONS),
@@ -62,19 +60,31 @@ N_ACTIONS = 1
 def test_entropy(dist):
     # The entropy can be approximated by averaging the negative log likelihood
     # mean negative log likelihood == differential entropy
-    n_samples = int(5e6)
-    n_features = 3
     set_random_seed(1)
-    state = th.rand(n_samples, n_features)
-    deterministic_actions = th.rand(n_samples, N_ACTIONS)
-    _, log_std = dist.proba_distribution_net(n_features, log_std_init=th.log(th.tensor(0.2)))
+    state = th.rand(N_SAMPLES, N_FEATURES)
+    deterministic_actions = th.rand(N_SAMPLES, N_ACTIONS)
+    _, log_std = dist.proba_distribution_net(N_FEATURES, log_std_init=th.log(th.tensor(0.2)))
 
     if isinstance(dist, DiagGaussianDistribution):
         actions, dist = dist.proba_distribution(deterministic_actions, log_std)
     else:
-        dist.sample_weights(log_std, batch_size=n_samples)
+        dist.sample_weights(log_std, batch_size=N_SAMPLES)
         actions, dist = dist.proba_distribution(deterministic_actions, log_std, state)
 
     entropy = dist.entropy()
     log_prob = dist.log_prob(actions)
     assert th.allclose(entropy.mean(), -log_prob.mean(), rtol=5e-3)
+
+
+def test_categorical():
+    # The entropy can be approximated by averaging the negative log likelihood
+    # mean negative log likelihood == entropy
+    dist = CategoricalDistribution(N_ACTIONS)
+    set_random_seed(1)
+    state = th.rand(N_SAMPLES, N_FEATURES)
+    action_logits = th.rand(N_SAMPLES, N_ACTIONS)
+    actions, dist = dist.proba_distribution(action_logits)
+
+    entropy = dist.entropy()
+    log_prob = dist.log_prob(actions)
+    assert th.allclose(entropy.mean(), -log_prob.mean(), rtol=1e-4)

tests/test_sde.py

@@ -22,7 +22,6 @@ def test_state_dependent_exploration_grad():
 
     state = th.rand(n_states, state_dim)
     mu = th.ones(action_dim)
-    # print(weights.shape, state.shape)
     noise = th.mm(state, weights)
 
     action = mu + noise

torchy_baselines/common/distributions.py

@@ -38,6 +38,22 @@ class Distribution(object):
         raise NotImplementedError
 
 
+def sum_independent_dims(tensor: th.Tensor) -> th.Tensor:
+    """
+    Continuous actions are usually considered to be independent,
+    so we can sum the components for the ``log_prob``
+    or the entropy.
+
+    :param tensor: (th.Tensor) shape: (n_batch, n_actions) or (n_batch,)
+    :return: (th.Tensor) shape: (n_batch,)
+    """
+    if len(tensor.shape) > 1:
+        tensor = tensor.sum(axis=1)
+    else:
+        tensor = tensor.sum()
+    return tensor
+
+
 class DiagGaussianDistribution(Distribution):
     """
     Gaussian distribution with diagonal covariance matrix,
@@ -95,7 +111,7 @@ class DiagGaussianDistribution(Distribution):
         return self.distribution.rsample()
 
     def entropy(self) -> th.Tensor:
-        return self.distribution.entropy()
+        return sum_independent_dims(self.distribution.entropy())
 
     def log_prob_from_params(self, mean_actions: th.Tensor, log_std: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
         """
@@ -113,18 +129,14 @@ class DiagGaussianDistribution(Distribution):
     def log_prob(self, action: th.Tensor) -> th.Tensor:
         """
         Get the log probabilty of an action given a distribution.
-        Note that you must call `proba_distribution()` method
+        Note that you must call ``proba_distribution()`` method
         before.
 
         :param action: (th.Tensor)
         :return: (th.Tensor)
         """
         log_prob = self.distribution.log_prob(action)
-        if len(log_prob.shape) > 1:
-            log_prob = log_prob.sum(axis=1)
-        else:
-            log_prob = log_prob.sum()
-        return log_prob
+        return sum_independent_dims(log_prob)
 
 
 class SquashedDiagGaussianDistribution(DiagGaussianDistribution):
@@ -243,14 +255,14 @@ class StateDependentNoiseDistribution(Distribution):
     :param action_dim: (int) Number of continuous actions
     :param full_std: (bool) Whether to use (n_features x n_actions) parameters
         for the std instead of only (n_features,)
-    :param use_expln: (bool) Use `expln()` function instead of `exp()` to ensure
+    :param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` to ensure
         a positive standard deviation (cf paper). It allows to keep variance
-        above zero and prevent it from growing too fast. In practice, `exp()` is usually enough.
+        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
     :param squash_output: (bool) Whether to squash the output using a tanh function,
         this allows to ensure boundaries.
     :param learn_features: (bool) Whether to learn features for SDE or not.
         This will enable gradients to be backpropagated through the features
-        `latent_sde` in the code.
+        ``latent_sde`` in the code.
     :param epsilon: (float) small value to avoid NaN due to numerical imprecision.
     """
 
@@ -396,7 +408,7 @@ class StateDependentNoiseDistribution(Distribution):
         # entropy needs to be estimated using -log_prob.mean()
         if self.bijector is not None:
             return None
-        return self.distribution.entropy()
+        return sum_independent_dims(self.distribution.entropy())
 
     def log_prob_from_params(self, mean_actions: th.Tensor,
                              log_std: th.Tensor,
@@ -412,11 +424,8 @@ class StateDependentNoiseDistribution(Distribution):
             gaussian_action = action
         # log likelihood for a gaussian
         log_prob = self.distribution.log_prob(gaussian_action)
-
-        if len(log_prob.shape) > 1:
-            log_prob = log_prob.sum(axis=1)
-        else:
-            log_prob = log_prob.sum()
+        # Sum along action dim
+        log_prob = sum_independent_dims(log_prob)
 
         if self.bijector is not None:
             # Squash correction (from original SAC implementation)

torchy_baselines/common/vec_env/__init__.py

@@ -3,8 +3,8 @@ import typing
 from typing import Optional
 from copy import deepcopy
 
-from torchy_baselines.common.vec_env.base_vec_env import AlreadySteppingError, NotSteppingError,\
-    VecEnv, VecEnvWrapper, CloudpickleWrapper
+from torchy_baselines.common.vec_env.base_vec_env import (AlreadySteppingError, NotSteppingError,
+                                                          VecEnv, VecEnvWrapper, CloudpickleWrapper)
 from torchy_baselines.common.vec_env.dummy_vec_env import DummyVecEnv
 from torchy_baselines.common.vec_env.subproc_vec_env import SubprocVecEnv
 from torchy_baselines.common.vec_env.vec_frame_stack import VecFrameStack

torchy_baselines/version.txt

@@ -1 +1 @@
-0.2.5
+0.2.6