fixed sac recurrent tf simple rl

ml-agents/mlagents/trainers/sac/optimizer_torch.py

             ]
 
         policy_params = list(self.policy.actor_critic.network_body.parameters()) + list(
-            self.policy.actor_critic.distribution.parameters()
+            self.policy.actor_critic.action_model.parameters()
         )
         value_params = list(self.value_network.parameters()) + list(
             self.policy.actor_critic.critic.parameters()

ml-agents/mlagents/trainers/tests/tensorflow/test_simple_rl.py

 
 @pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
 def test_recurrent_sac(action_sizes):
-    step_size = 0.2 if action_sizes else 0.5
+    step_size = 0.2 if action_sizes == (0, 1) else 0.5
     env = MemoryEnvironment(
         [BRAIN_NAME], action_sizes=action_sizes, step_size=step_size
     )