Merge branch 'flatland-paper-baselines' of...

Merge branch 'flatland-paper-baselines' of gitlab.aicrowd.com:flatland/neurips2020-flatland-baselines into flatland-paper-baselines

envs/flatland/eval_configs/default_render.yaml

+evaluation_num_workers: 2
+# Evaluation wokers for the evaluation run.
+evaluation_interval: 50
+# Episodes each time evaluation runs. Reduced episodes for rendering
+evaluation_num_episodes: 5
+# Override the env config for evaluation.
+evaluation_config:
+   explore: False
+   env_config:
+       seed: 100
+       render: human
\ No newline at end of file

envs/flatland/eval_configs/test_eval.yaml → envs/flatland/eval_configs/test_render.yaml

@@ -7,4 +7,6 @@ evaluation_num_episodes: 2
 evaluation_config:
    explore: False
    env_config:
-       seed: 100
\ No newline at end of file
+       seed: 100
+       render: human
+       # video_dir: small_tree_video
\ No newline at end of file

envs/flatland/utils/gym_env_wrappers.py

@@ -447,9 +447,10 @@ class FlatlandRenderWrapper(RailEnv,gym.Env):
                                        show_debug=False,
                                        screen_height=600,  # Adjust these parameters to fit your resolution
                                        screen_width=800)  # Adjust these parameters to fit your resolution
+        self.renderer.show = False
 
     def update_renderer(self, mode='human'):
-        image = self.renderer.render_env(show=True, show_observations=False, show_predictions=False,
+        image = self.renderer.render_env(show=False, show_observations=False, show_predictions=False,
                                              return_image=True)
         return image[:,:,:3]
 
@@ -462,12 +463,12 @@ class FlatlandRenderWrapper(RailEnv,gym.Env):
         super().close()
         if self.renderer:
             try:
-                self.renderer.close_window()
-                self.renderer = None
+                if self.renderer.show:
+                    self.renderer.close_window()
             except Exception as e:
                 # This is since the last step(Due to a stopping criteria) is skipped by rllib
                 # Due to this done is not true and the env does not close
                 # Finally the env is closed when RLLib exits but at that time there is no window
                 # and hence the error
                 print("Could Not close window due to:",e)
-
+            self.renderer = None

recording/apex.yaml → recording/apex_train_render.yaml

@@ -33,7 +33,7 @@ flatland-sparse-small-action-mask-tree-fc-apex:
 
             wandb:
                 project: neurips2020-flatland-baselines
-                entity: nilabha2007
+                entity: aicrowd
                 tags: ["small_v0", "new_tree_obs", "apex", "skip_no_choice_cells",
                        "action_mask"] # TODO should be set programmatically
 

recording/ppo.yaml → recording/ppo_eval_render.yaml

@@ -19,7 +19,7 @@ flatland-render-test:
         rollout_fragment_length: 50  # 100
         sgd_minibatch_size: 100  # 500
         num_sgd_iter: 10
-        num_workers: 2
+        num_workers: 1
         num_envs_per_worker: 1
         batch_mode: truncate_episodes
         observation_filter: NoFilter
@@ -31,20 +31,21 @@ flatland-render-test:
             observation: new_tree
             skip_no_choice_cells: True
             available_actions_obs: True
-            render: human
+            # render: human
             # For saving videos in custom folder and to wandb.
             # By default if not specified folder is flatland
-            video_dir: small_tree_video
+            # video_dir: small_tree_video
             observation_config:
                 max_depth: 2
                 shortest_path_max_depth: 30
 
             generator: sparse_rail_generator
             generator_config: small_v0
+            eval_generator: test_render
 
             wandb:
                 project: neurips2020-flatland-baselines
-                entity: nilabha2007
+                entity: aicrowd
                 tags: ["small_v0", "tree_obs"] # TODO should be set programmatically
                 # monitor_gym: True # Wandb video doesn't seem to work
 

train.py

@@ -189,9 +189,14 @@ def run(args, parser):
             }
 
         if args.eval:
-            eval_configs = get_eval_config(exp['config'].get('env_config',\
-                           {}).get('eval_generator',"default"))
+            eval_configs_file = exp['config'].get('env_config',\
+                                    {}).get('eval_generator',"default")
+            if args.record:
+                eval_configs_file = exp['config'].get('env_config',\
+                        {}).get('eval_generator',"default_render")
+            eval_configs = get_eval_config(eval_configs_file)
             eval_seed = eval_configs.get('evaluation_config',{}).get('env_config',{}).get('seed')
+            eval_render = eval_configs.get('evaluation_config',{}).get('env_config',{}).get('render')
 
             # add evaluation config to the current config
             exp['config'] = merge_dicts(exp['config'],eval_configs)
@@ -201,7 +206,13 @@ def run(args, parser):
                 if eval_seed and eval_env_config:
                     # We override the env seed from the evaluation config
                     eval_env_config['seed'] = eval_seed
-
+                if eval_render and eval_env_config:
+                    # We override the env render from the evaluation config
+                    eval_env_config['render'] = eval_render
+                    # Set video_dir if it exists
+                    eval_render_dir = eval_configs.get('evaluation_config',{}).get('env_config',{}).get('video_dir')
+                    if eval_render_dir:
+                        eval_env_config['video_dir'] = eval_render_dir
                 # Remove any wandb related configs
                 if eval_env_config:
                     if eval_env_config.get('wandb'):

utils/argparser.py

@@ -105,13 +105,20 @@ def create_parser(parser_creator=None):
         "--eval",
         action="store_true",
         help="Whether to run evaluation. Default evaluation config is default.yaml "
-        "to use custom evaluation config set (eval_generator:high_eval) under configs")
+        "to use custom evaluation config set (eval_generator:test_eval) under configs")
     parser.add_argument(
         "-i",
         "--custom-fn",
         action="store_true",
         help="Whether the experiment uses a custom function for training"
         "Default custom function is imitation_ppo_train_fn")
+    parser.add_argument(
+        "-r",
+        "--record",
+        action="store_true",
+        help="Whether the experiment requires video recording during evaluation"
+        "Default evaluation config is default_render.yaml "
+        "Can also be done via custom evaluation config set (eval_generator:test_render) under configs")
     parser.add_argument(
         "--bind-all",
         action="store_true",