diff --git a/test/base/test_policy.py b/test/base/test_policy.py
index 4d26905c3..8911bab16 100644
--- a/test/base/test_policy.py
+++ b/test/base/test_policy.py
@@ -4,7 +4,9 @@
 import torch
 from torch.distributions import Categorical, Distribution, Independent, Normal
 
+from tianshou.data import Batch
 from tianshou.policy import BasePolicy, PPOPolicy
+from tianshou.policy.base import RandomActionPolicy
 from tianshou.utils.net.common import ActorCritic, Net
 from tianshou.utils.net.continuous import ActorProb, Critic
 from tianshou.utils.net.discrete import Actor
@@ -77,3 +79,45 @@ def test_get_action(self, policy: PPOPolicy) -> None:
         actions = [policy.compute_action(sample_obs) for _ in range(10)]
         # check that the actions are the same in deterministic mode
         assert len(set(map(_to_hashable, actions))) == 1
+
+    @staticmethod
+    def test_random_policy_discrete_actions() -> None:
+        action_space = gym.spaces.Discrete(3)
+        policy = RandomActionPolicy(action_space=action_space)
+
+        # forward of actor returns discrete probabilities, in compliance with the overall discrete actor
+        action_probs = policy.actor(np.zeros((10, 2)))[0]
+        assert np.allclose(action_probs, 1 / 3 * np.ones((10, 3)))
+
+        actions = []
+        for _ in range(10):
+            action = policy.compute_action(np.array([0]))
+            assert action_space.contains(action)
+            actions.append(action)
+
+        # not all actions are the same
+        assert len(set(actions)) > 1
+
+        # test batched forward
+        action_batch = policy(Batch(obs=np.zeros((10, 2))))
+        assert action_batch.act.shape == (10,)
+        assert len(set(action_batch.act.tolist())) > 1
+
+    @staticmethod
+    def test_random_policy_continuous_actions() -> None:
+        action_space = gym.spaces.Box(low=-1, high=1, shape=(3,))
+        policy = RandomActionPolicy(action_space=action_space)
+
+        actions = []
+        for _ in range(10):
+            action = policy.compute_action(np.array([0]))
+            assert action_space.contains(action)
+            actions.append(action)
+
+        # not all actions are the same
+        assert len(set(map(_to_hashable, actions))) > 1
+
+        # test batched forward
+        action_batch = policy(Batch(obs=np.zeros((10, 2))))
+        assert action_batch.act.shape == (10, 3)
+        assert len(set(map(_to_hashable, action_batch.act))) > 1
diff --git a/tianshou/policy/base.py b/tianshou/policy/base.py
index fb3c71e0f..d21010bf0 100644
--- a/tianshou/policy/base.py
+++ b/tianshou/policy/base.py
@@ -712,7 +712,7 @@ def forward(
         state: dict | BatchProtocol | np.ndarray | None = None,
         **kwargs: Any,
     ) -> ActStateBatchProtocol:
-        act, next_state = self.actor(batch.obs, state)
+        act, next_state = self.actor.compute_action_batch(batch.obs), state
         return cast(ActStateBatchProtocol, Batch(act=act, state=next_state))
 
     def learn(self, batch: RolloutBatchProtocol, *args: Any, **kwargs: Any) -> TrainingStats:
diff --git a/tianshou/utils/net/common.py b/tianshou/utils/net/common.py
index 7510ae7e7..243a04093 100644
--- a/tianshou/utils/net/common.py
+++ b/tianshou/utils/net/common.py
@@ -7,7 +7,7 @@
 from gymnasium import spaces
 from torch import nn
 
-from tianshou.data.batch import Batch
+from tianshou.data.batch import Batch, BatchProtocol
 from tianshou.data.types import RecurrentStateBatch
 from tianshou.utils.space_info import ActionSpaceInfo
 
@@ -661,9 +661,13 @@ def get_preprocess_net(self) -> nn.Module:
     def get_output_dim(self) -> int:
         return self.space_info.action_dim
 
+    @property
+    def is_discrete(self) -> bool:
+        return isinstance(self.action_space, spaces.Discrete)
+
     def forward(
         self,
-        obs: np.ndarray | torch.Tensor,
+        obs: np.ndarray | torch.Tensor | BatchProtocol,
         state: Any | None = None,
         info: dict[str, Any] | None = None,
     ) -> tuple[np.ndarray, Any | None]:
@@ -675,6 +679,14 @@ def forward(
             action = 1 / self.action_space.n * np.ones((batch_size, self.action_space.n))
         return action, state
 
+    def compute_action_batch(self, obs: np.ndarray | torch.Tensor | BatchProtocol) -> np.ndarray:
+        if self.is_discrete:
+            # Different from forward which returns discrete probabilities, see comment there
+            assert isinstance(self.action_space, spaces.Discrete)  # for mypy
+            return np.random.randint(low=0, high=self.action_space.n, size=len(obs))
+        else:
+            return self.forward(obs)[0]
+
 
 def getattr_with_matching_alt_value(obj: Any, attr_name: str, alt_value: T | None) -> T:
     """Gets the given attribute from the given object or takes the alternative value if it is not present.