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Fix stable_baselines3/common/distributions.py type hint #123

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Fix stable_baselines3/common/distributions.py type hint #123

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3ed9015
Use approx entropy when no analytical form is known
qgallouedec Nov 29, 2022
b0a0954
Drop entropy is None case
qgallouedec Nov 29, 2022
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7 changes: 6 additions & 1 deletion sb3_contrib/common/maskable/policies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -280,7 +280,12 @@ def evaluate_actions(
distribution.apply_masking(action_masks)
log_prob = distribution.log_prob(actions)
values = self.value_net(latent_vf)
return values, log_prob, distribution.entropy()
try:
entropy = distribution.entropy()
except NotImplementedError:
# When no analytical form, entropy needs to be estimated using -log_prob.mean()
entropy = -log_prob.mean(dim=1)
return values, log_prob, entropy

def get_distribution(self, obs: th.Tensor, action_masks: Optional[np.ndarray] = None) -> MaskableDistribution:
"""
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7 changes: 6 additions & 1 deletion sb3_contrib/common/recurrent/policies.py
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Original file line number Diff line number Diff line change
Expand Up @@ -329,7 +329,12 @@ def evaluate_actions(
distribution = self._get_action_dist_from_latent(latent_pi)
log_prob = distribution.log_prob(actions)
values = self.value_net(latent_vf)
return values, log_prob, distribution.entropy()
try:
entropy = distribution.entropy()
except NotImplementedError:
# When no analytical form, entropy needs to be estimated using -log_prob.mean()
entropy = -log_prob.mean(dim=1)
return values, log_prob, entropy

def _predict(
self,
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6 changes: 1 addition & 5 deletions sb3_contrib/ppo_mask/ppo_mask.py
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Expand Up @@ -446,11 +446,7 @@ def train(self) -> None:
value_losses.append(value_loss.item())

# Entropy loss favor exploration
if entropy is None:
# Approximate entropy when no analytical form
entropy_loss = -th.mean(-log_prob)
else:
entropy_loss = -th.mean(entropy)
entropy_loss = -th.mean(entropy)

entropy_losses.append(entropy_loss.item())

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6 changes: 1 addition & 5 deletions sb3_contrib/ppo_recurrent/ppo_recurrent.py
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Expand Up @@ -389,11 +389,7 @@ def train(self) -> None:
value_losses.append(value_loss.item())

# Entropy loss favor exploration
if entropy is None:
# Approximate entropy when no analytical form
entropy_loss = -th.mean(-log_prob[mask])
else:
entropy_loss = -th.mean(entropy[mask])
entropy_loss = -th.mean(entropy[mask])

entropy_losses.append(entropy_loss.item())

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