Fix PPO tests

ml-agents/mlagents/trainers/tests/torch/test_ppo.py

             RewardSignalUtil.value_estimates_key("extrinsic"),
         ],
     )
+    # Copy memories to critic memories
+    copy_buffer_fields(update_buffer, BufferKey.MEMORY, [BufferKey.CRITIC_MEMORY])
 
     return_stats = optimizer.update(
         update_buffer,
             RewardSignalUtil.value_estimates_key("curiosity"),
         ],
     )
+    # Copy memories to critic memories
+    copy_buffer_fields(update_buffer, BufferKey.MEMORY, [BufferKey.CRITIC_MEMORY])
 
     optimizer.update(
         update_buffer,
         action_spec=DISCRETE_ACTION_SPEC if discrete else CONTINUOUS_ACTION_SPEC,
         max_step_complete=True,
     )
-    run_out, final_value_out = optimizer.get_trajectory_value_estimates(
+    run_out, final_value_out, all_memories = optimizer.get_trajectory_value_estimates(
+    if all_memories is not None:
+        assert len(all_memories) == 15
-    run_out, final_value_out = optimizer.get_trajectory_value_estimates(
+    run_out, final_value_out, _ = optimizer.get_trajectory_value_estimates(
         trajectory.to_agentbuffer(), trajectory.next_obs, done=True
     )
     for key, val in final_value_out.items():
     # Check if we ignore terminal states properly
     optimizer.reward_signals["extrinsic"].use_terminal_states = False
-    run_out, final_value_out = optimizer.get_trajectory_value_estimates(
+    run_out, final_value_out, _ = optimizer.get_trajectory_value_estimates(
         trajectory.to_agentbuffer(), trajectory.next_obs, done=False
     )
     for key, val in final_value_out.items():