Rebuttal (#15)

* pg+nuplan train

* Need map

* use gym wrapper

* use createGymWrapper

* doc

* use all scenarios!

* update 80000 scenario

* train script

scenarionet_training/scripts/train_pg.py

@@ -1,14 +1,14 @@
 import os.path
 from ray.tune import grid_search
 from metadrive.envs.scenario_env import ScenarioEnv
-from metadrive.envs.gym_wrapper import GymEnvWrapper
+from metadrive.envs.gym_wrapper import createGymWrapper
 from scenarionet import SCENARIONET_REPO_PATH, SCENARIONET_DATASET_PATH
 from scenarionet_training.train_utils.multi_worker_PPO import MultiWorkerPPO
 from scenarionet_training.train_utils.utils import train, get_train_parser, get_exp_name
 
 config = dict(
-    env=GymEnvWrapper,
-    env_config=dict(inner_class=ScenarioEnv, inner_config=dict(
+    env=createGymWrapper(ScenarioEnv),
+    env_config=dict(
         # scenario
         start_scenario_index=0,
         num_scenarios=40000,
@@ -21,7 +21,7 @@ config = dict(
         # episodes_to_evaluate_curriculum=400,  # default=num_scenarios/curriculum_level
 
         # traffic & light
-        reactive_traffic=False,
+        reactive_traffic=True,
         no_static_vehicles=True,
         no_light=True,
         static_traffic_object=True,
@@ -41,7 +41,7 @@ config = dict(
 
         vehicle_config=dict(side_detector=dict(num_lasers=0))
 
-    )),
+    ),
 
     # ===== Evaluation =====
     evaluation_interval=15,