fix test agent processor

ml-agents/mlagents/trainers/tests/test_agent_processor.py

     AgentManagerQueue,
 )
 from mlagents.trainers.action_info import ActionInfo
+from mlagents.trainers.torch.action_log_probs import LogProbsTuple
-from mlagents_envs.base_env import ActionSpec
+from mlagents_envs.base_env import ActionSpec, ActionTuple
 
 
 def create_mock_policy():
     )
 
     fake_action_outputs = {
-        "action": {"continuous_action": [0.1, 0.1]},
+        "action": ActionTuple(continuous=np.array([[0.1], [0.1]])),
-        "log_probs": {"continuous_log_probs": [0.1, 0.1]},
+        "log_probs": LogProbsTuple(continuous=np.array([[0.1], [0.1]])),
     }
     mock_decision_steps, mock_terminal_steps = mb.create_mock_steps(
         num_agents=2,
     fake_action_info = ActionInfo(
-        action={"continuous_action": [0.1, 0.1]},
+        action=ActionTuple(continuous=np.array([[0.1], [0.1]])),
         value=[0.1, 0.1],
         outputs=fake_action_outputs,
         agent_ids=mock_decision_steps.agent_id,
         max_trajectory_length=5,
         stats_reporter=StatsReporter("testcat"),
     )
-
-        "action": {"continuous_action": [0.1]},
+        "action": ActionTuple(continuous=np.array([[0.1]])),
-        "log_probs": {"continuous_log_probs": [0.1]},
+        "log_probs": LogProbsTuple(continuous=np.array([[0.1]])),
+
     mock_decision_step, mock_terminal_step = mb.create_mock_steps(
         num_agents=1,
         observation_shapes=[(8,)],
         done=True,
     )
     fake_action_info = ActionInfo(
-        action={"continuous_action": [0.1]},
+        action=ActionTuple(continuous=np.array([[0.1]])),
         value=[0.1],
         outputs=fake_action_outputs,
         agent_ids=mock_decision_step.agent_id,
                 mock_decision_step, mock_terminal_step, _ep, fake_action_info
             )
             add_calls.append(
-                mock.call([get_global_agent_id(_ep, 0)], {"continuous_action": [0.1]})
+                mock.call([get_global_agent_id(_ep, 0)], fake_action_outputs["action"])
             )
         processor.add_experiences(
             mock_done_decision_step, mock_done_terminal_step, _ep, fake_action_info
         max_trajectory_length=5,
         stats_reporter=StatsReporter("testcat"),
     )
-
-        "action": {"continuous_action": [0.1]},
+        "action": ActionTuple(continuous=np.array([[0.1]])),
-        "log_probs": {"continuous_log_probs": [0.1]},
+        "log_probs": LogProbsTuple(continuous=np.array([[0.1]])),
+
     mock_decision_step, mock_terminal_step = mb.create_mock_steps(
         num_agents=1,
         observation_shapes=[(8,)],
-        action={"continuous_action": [0.1]},
+        action=ActionTuple(continuous=np.array([[0.1]])),
         value=[0.1],
         outputs=fake_action_outputs,
         agent_ids=mock_decision_step.agent_id,