Using dist.mode instead of logits.argmax. More compact.

test/continuous/test_sac_with_il.py

@@ -3,7 +3,6 @@
 
 import gymnasium as gym
 import numpy as np
-import pytest
 import torch
 from torch.utils.tensorboard import SummaryWriter
 
@@ -58,7 +57,6 @@ def get_args() -> argparse.Namespace:
     return parser.parse_known_args()[0]
 
 
-@pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform")
 def test_sac_with_il(args: argparse.Namespace = get_args()) -> None:
     # if you want to use python vector env, please refer to other test scripts
     # train_envs = env = envpool.make_gymnasium(args.task, num_envs=args.training_num, seed=args.seed)

tianshou/policy/modelfree/discrete_sac.py

@@ -109,7 +109,7 @@ def forward(  # type: ignore
         logits, hidden = self.actor(batch.obs, state=state, info=batch.info)
         dist = Categorical(logits=logits)
         if self.deterministic_eval and not self.training:
-            act = logits.argmax(axis=-1)
+            act = dist.mode
         else:
             act = dist.sample()
         return Batch(logits=logits, act=act, state=hidden, dist=dist)

tianshou/policy/modelfree/pg.py

@@ -158,19 +158,6 @@ def process_fn(
         batch: BatchWithReturnsProtocol
         return batch
 
-    def _get_deterministic_action(self, logits: torch.Tensor) -> torch.Tensor:
-        if self.action_type == "discrete":
-            return logits.argmax(-1)
-        if self.action_type == "continuous":
-            # assume that the mode of the distribution is the first element
-            # of the actor's output (the "logits")
-            return logits[0]
-        raise RuntimeError(
-            f"Unknown action type: {self.action_type}. "
-            f"This should not happen and might be a bug."
-            f"Supported action types are: 'discrete' and 'continuous'.",
-        )
-
     def forward(
         self,
         batch: ObsBatchProtocol,
@@ -198,7 +185,7 @@ def forward(
 
         # in this case, the dist is unused!
         if self.deterministic_eval and not self.training:
-            act = self._get_deterministic_action(logits)
+            act = dist.mode
         else:
             act = dist.sample()
         result = Batch(logits=logits, act=act, state=hidden, dist=dist)

tianshou/policy/modelfree/redq.py

@@ -153,7 +153,10 @@ def forward(  # type: ignore
         loc_scale, h = self.actor(batch.obs, state=state, info=batch.info)
         loc, scale = loc_scale
         dist = Independent(Normal(loc, scale), 1)
-        act = loc if self.deterministic_eval and not self.training else dist.rsample()
+        if self.deterministic_eval and not self.training:
+            act = dist.mode
+        else:
+            act = dist.rsample()
         log_prob = dist.log_prob(act).unsqueeze(-1)
         # apply correction for Tanh squashing when computing logprob from Gaussian
         # You can check out the original SAC paper (arXiv 1801.01290): Eq 21.

tianshou/policy/modelfree/sac.py

@@ -56,7 +56,7 @@ class SACPolicy(DDPGPolicy[TSACTrainingStats], Generic[TSACTrainingStats]):  # t
         This is useful when solving "hard exploration" problems.
         "default" is equivalent to GaussianNoise(sigma=0.1).
     :param deterministic_eval: whether to use deterministic action
-        (mean of Gaussian policy) in evaluation mode instead of stochastic
+        (mode of Gaussian policy) in evaluation mode instead of stochastic
         action sampled by the policy. Does not affect training.
     :param action_scaling: whether to map actions from range [-1, 1]
         to range[action_spaces.low, action_spaces.high].
@@ -177,7 +177,7 @@ def forward(  # type: ignore
         assert isinstance(logits, tuple)
         dist = Independent(Normal(*logits), 1)
         if self.deterministic_eval and not self.training:
-            act = logits[0]
+            act = dist.mode
         else:
             act = dist.rsample()
         log_prob = dist.log_prob(act).unsqueeze(-1)