adjust step size

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

         vec_obs_size=OBS_SIZE,
         var_len_obs_size=VAR_LEN_SIZE,
         action_sizes=(1, 0),
+        gail=False,
+        self.gail = gail
         self.num_visual = num_visual
         self.num_vector = num_vector
         self.num_var_len = num_var_len
         return action_mask
 
     def _compute_reward(self, name: str, done: bool) -> float:
-        if done:
+        if self.gail:
+            # Subtract large positive constant to eliminate survivor bias in GAIL
+            reward = -0.2
+        elif done:
             reward = 0.0
             for _pos in self.positions[name]:
                 reward += (SUCCESS_REWARD * _pos * self.goal[name]) / len(

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

     SelfPlaySettings,
     BehavioralCloningSettings,
     GAILSettings,
+    RewardSignalSettings,
     RewardSignalType,
     EncoderType,
 )
 @pytest.mark.parametrize("trainer_config", [PPO_TORCH_CONFIG, SAC_TORCH_CONFIG])
 def test_gail(simple_record, action_sizes, trainer_config):
     demo_path = simple_record(action_sizes)
-    env = SimpleEnvironment([BRAIN_NAME], action_sizes=action_sizes, step_size=0.2)
+    env = SimpleEnvironment(
+        [BRAIN_NAME], action_sizes=action_sizes, step_size=0.3, gail=True
+    )
-        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
+        RewardSignalType.GAIL: GAILSettings(
+            strength=0.05, encoding_size=32, demo_path=demo_path
+        ),
+        RewardSignalType.EXTRINSIC: RewardSignalSettings(),
     }
     config = attr.evolve(
         trainer_config,
     )
-    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=-1.5)
 
 
 @pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
         num_vector=0,
         action_sizes=action_sizes,
         step_size=0.3,
+        gail=True,
-        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
+        RewardSignalType.GAIL: GAILSettings(
+            strength=0.05, encoding_size=32, demo_path=demo_path
+        ),
+        RewardSignalType.EXTRINSIC: RewardSignalSettings(),
     }
     hyperparams = attr.evolve(PPO_TORCH_CONFIG.hyperparameters, learning_rate=5e-3)
     config = attr.evolve(
         behavioral_cloning=bc_settings,
         max_steps=1000,
     )
-    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=-1.5)
 
 
 @pytest.mark.parametrize("action_sizes", [(0, 1), (1, 0)])
         num_visual=1,
         num_vector=0,
         action_sizes=action_sizes,
-        step_size=0.2,
+        step_size=0.3,
+        gail=True,
-        RewardSignalType.GAIL: GAILSettings(encoding_size=32, demo_path=demo_path)
+        RewardSignalType.GAIL: GAILSettings(
+            strength=0.05, encoding_size=32, demo_path=demo_path
+        ),
+        RewardSignalType.EXTRINSIC: RewardSignalSettings(),
     }
     hyperparams = attr.evolve(
         SAC_TORCH_CONFIG.hyperparameters, learning_rate=3e-4, batch_size=16
         behavioral_cloning=bc_settings,
         max_steps=500,
     )
-    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=0.9)
+    check_environment_trains(env, {BRAIN_NAME: config}, success_threshold=-1.5)