added num_gpus as arg, first try sh info callback

examples/shields/rl/15_train_eval_tune.py

@@ -68,34 +68,34 @@ def trial_name_creator(trial : Trial):
 def ppo(args):
     register_minigrid_shielding_env(args)
     logdir = args.log_dir
-    
+
     config = (PPOConfig()
         .rollouts(num_rollout_workers=args.workers)
-        .resources(num_gpus=0)
+        .resources(num_gpus=args.num_gpus)
         .environment( env="mini-grid-shielding",
                       env_config={"name": args.env,
-                                  "args": args,                                  
+                                  "args": args,
                                   "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Training,
                                   },)
         .framework("torch")
-        .callbacks(MyCallbacks)
-        .evaluation(evaluation_config={ 
+        .callbacks(MyCallbacks, ShieldInfoCallback(logdir, [1,12])
+        .evaluation(evaluation_config={
                                        "evaluation_interval": 1,
                                         "evaluation_duration": 10,
                                         "evaluation_num_workers":1,
-                                        "env": "mini-grid-shielding", 
-                                        "env_config": {"name": args.env, 
-                                                       "args": args, 
-                                                       "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Evaluation}})        
+                                        "env": "mini-grid-shielding",
+                                        "env_config": {"name": args.env,
+                                                       "args": args,
+                                                       "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Evaluation}})
         .rl_module(_enable_rl_module_api = False)
         .debugging(logger_config={
-            "type": UnifiedLogger, 
+            "type": UnifiedLogger,
             "logdir": logdir
         })
         .training(_enable_learner_api=False ,model={
-            "custom_model": "shielding_model"      
+            "custom_model": "shielding_model"
         }))
-    
+
     tuner = tune.Tuner("PPO",
                        tune_config=tune.TuneConfig(
                            metric="episode_reward_mean",

examples/shields/rl/callbacks.py

@@ -15,7 +15,16 @@ from ray.rllib.algorithms.callbacks import DefaultCallbacks, make_multi_callback
 
 import matplotlib.pyplot as plt
 
+import tensorflow as tf
 
+class ShieldInfoCallback(DefaultCallbacks):
+    def on_episode_start(self, log_dir, data) -> None:
+        file_writer = tf.summary.create_file_writer(log_dir)
+        with file_writer.as_default():
+            tf.summary.text("first_text", str(data), step=0)
+
+    def on_episode_step(self) -> None:
+        pass
 
 class MyCallbacks(DefaultCallbacks):
     def on_episode_start(self, *, worker: RolloutWorker, base_env: BaseEnv, policies: Dict[PolicyID, Policy], episode, env_index, **kwargs) -> None:

examples/shields/rl/helpers.py

@@ -132,6 +132,7 @@ def parse_arguments(argparse):
     parser.add_argument("--formula", default="Pmax=? [G !\"AgentIsInLavaAndNotDone\"]")  # formula_str = "Pmax=? [G ! \"AgentIsInGoalAndNotDone\"]"
     # parser.add_argument("--formula", default="<<Agent>> Pmax=? [G <= 4 !\"AgentRanIntoAdversary\"]")
     parser.add_argument("--workers", type=int, default=1)
+    parser.add_argument("--num_gpus", type=float, default=0)
     parser.add_argument("--shielding", type=ShieldingConfig, choices=list(ShieldingConfig), default=ShieldingConfig.Full)
     parser.add_argument("--steps", default=20_000, type=int)
     parser.add_argument("--expname", default="exp")