Fix AgentProcessor tests

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

 def create_mock_policy():
     mock_policy = mock.Mock()
     mock_policy.reward_signals = {}
-    mock_policy.retrieve_memories.return_value = np.zeros((1, 1), dtype=np.float32)
+    mock_policy.retrieve_previous_memories.return_value = np.zeros(
+        (1, 1), dtype=np.float32
+    )
     mock_policy.retrieve_previous_action.return_value = np.zeros((1, 1), dtype=np.int32)
     return mock_policy
 
     )
 
     fake_action_outputs = {
-        "action": ActionTuple(continuous=np.array([[0.1], [0.1]])),
+        "action": ActionTuple(continuous=np.array([[0.1], [0.1]], dtype=np.float32)),
-        "log_probs": LogProbsTuple(continuous=np.array([[0.1], [0.1]])),
+        "log_probs": LogProbsTuple(
+            continuous=np.array([[0.1], [0.1]], dtype=np.float32)
+        ),
     }
     mock_decision_steps, mock_terminal_steps = mb.create_mock_steps(
         num_agents=2,
         action_spec=ActionSpec.create_continuous(2),
     )
     fake_action_info = ActionInfo(
-        action=ActionTuple(continuous=np.array([[0.1], [0.1]])),
-        env_action=ActionTuple(continuous=np.array([[0.1], [0.1]])),
+        action=ActionTuple(continuous=np.array([[0.1], [0.1]], dtype=np.float32)),
+        env_action=ActionTuple(continuous=np.array([[0.1], [0.1]], dtype=np.float32)),
         outputs=fake_action_outputs,
         agent_ids=mock_decision_steps.agent_id,
     )
         stats_reporter=StatsReporter("testcat"),
     )
     fake_action_outputs = {
-        "action": ActionTuple(continuous=np.array([[0.1]])),
+        "action": ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
-        "log_probs": LogProbsTuple(continuous=np.array([[0.1]])),
+        "log_probs": LogProbsTuple(continuous=np.array([[0.1]], dtype=np.float32)),
     }
 
     mock_decision_step, mock_terminal_step = mb.create_mock_steps(
         done=True,
     )
     fake_action_info = ActionInfo(
-        action=ActionTuple(continuous=np.array([[0.1]])),
-        env_action=ActionTuple(continuous=np.array([[0.1]])),
+        action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        env_action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
         outputs=fake_action_outputs,
         agent_ids=mock_decision_step.agent_id,
     )
         stats_reporter=StatsReporter("testcat"),
     )
     fake_action_outputs = {
-        "action": ActionTuple(continuous=np.array([[0.1]])),
+        "action": ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
-        "log_probs": LogProbsTuple(continuous=np.array([[0.1]])),
+        "log_probs": LogProbsTuple(continuous=np.array([[0.1]], dtype=np.float32)),
     }
 
     mock_decision_step, mock_terminal_step = mb.create_mock_steps(
     )
     fake_action_info = ActionInfo(
-        action=ActionTuple(continuous=np.array([[0.1]])),
-        env_action=ActionTuple(continuous=np.array([[0.1]])),
+        action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
+        env_action=ActionTuple(continuous=np.array([[0.1]], dtype=np.float32)),
         outputs=fake_action_outputs,
         agent_ids=mock_decision_step.agent_id,
     )