fix proto test, revert gym and llapi tests

.yamato/protobuf-generation-test.yml

-
-test_linux_protobuf_generation:
+- test_linux_protobuf_generation:
   name: Protobuf Generation Tests
   agent:
     type: Unity::VM

ml-agents/tests/yamato/scripts/run_gym.py

     :param env_name: Name of the Unity environment binary to launch
     """
     u_env = UnityEnvironment(env_name, worker_id=1, no_graphics=True)
-    env = UnityToGymWrapper(u_env)
+    env = UnityToGymWrapper(u_env, use_visual=False)
 
     try:
         # Examine environment parameters
 
     try:
         env1 = UnityToGymWrapper(
-            UnityEnvironment(env_name, worker_id=1, no_graphics=True)
+            UnityEnvironment(env_name, worker_id=1, no_graphics=True), use_visual=False
-            UnityEnvironment(env_name, worker_id=1, no_graphics=True)
+            UnityEnvironment(env_name, worker_id=1, no_graphics=True), use_visual=False
-            UnityEnvironment(env_name, worker_id=2, no_graphics=True)
+            UnityEnvironment(env_name, worker_id=2, no_graphics=True), use_visual=False
         )
         env2.reset()
     finally:

ml-agents/tests/yamato/scripts/run_llapi.py

 import argparse
+import numpy as np
 
 from mlagents_envs.environment import UnityEnvironment
 from mlagents_envs.side_channel.engine_configuration_channel import (
         file_name=env_name,
         side_channels=[engine_configuration_channel],
         no_graphics=True,
-        additional_args=["-logFile", "-"],
+        args=["-logFile", "-"],
     )
 
     try:
         # Set the default brain to work with
-        group_name = list(env.behavior_specs.keys())[0]
-        group_spec = env.behavior_specs[group_name]
+        group_name = env.get_behavior_names()[0]
+        group_spec = env.get_behavior_spec(group_name)
 
         # Set the time scale of the engine
         engine_configuration_channel.set_configuration_parameters(time_scale=3.0)
 
         # Examine the number of observations per Agent
-        print("Number of observations : ", len(group_spec.observation_specs))
+        print("Number of observations : ", len(group_spec.observation_shapes))
-        vis_obs = any(
-            len(obs_spec.shape) == 3 for obs_spec in group_spec.observation_specs
-        )
+        vis_obs = any(len(shape) == 3 for shape in group_spec.observation_shapes)
         print("Is there a visual observation ?", vis_obs)
 
         # Examine the state space for the first observation for the first agent
             episode_rewards = 0
             tracked_agent = -1
             while not done:
-                action_tuple = group_spec.action_spec.random_action(len(decision_steps))
+                if group_spec.is_action_continuous():
+                    action = np.random.randn(
+                        len(decision_steps), group_spec.action_size
+                    )
+
+                elif group_spec.is_action_discrete():
+                    branch_size = group_spec.discrete_action_branches
+                    action = np.column_stack(
+                        [
+                            np.random.randint(
+                                0, branch_size[i], size=(len(decision_steps))
+                            )
+                            for i in range(len(branch_size))
+                        ]
+                    )
+                else:
+                    # Should never happen
+                    action = None
-                env.set_actions(group_name, action_tuple)
+                env.set_actions(group_name, action)
                 env.step()
                 decision_steps, terminal_steps = env.get_steps(group_name)
                 done = False
     """
     try:
         env1 = UnityEnvironment(
-            file_name=env_name,
-            base_port=5006,
-            no_graphics=True,
-            additional_args=["-logFile", "-"],
+            file_name=env_name, base_port=5006, no_graphics=True, args=["-logFile", "-"]
-            file_name=env_name,
-            base_port=5006,
-            no_graphics=True,
-            additional_args=["-logFile", "-"],
+            file_name=env_name, base_port=5006, no_graphics=True, args=["-logFile", "-"]
-            file_name=env_name,
-            base_port=5007,
-            no_graphics=True,
-            additional_args=["-logFile", "-"],
+            file_name=env_name, base_port=5007, no_graphics=True, args=["-logFile", "-"]
         )
         env2.reset()
     finally: