added expname to grid path

examples/shields/rl/15_train_eval_tune.py

@@ -10,7 +10,7 @@ from ray.tune.logger import UnifiedLogger
 from ray.rllib.models import ModelCatalog
 from ray.tune.logger import pretty_print, UnifiedLogger, CSVLogger
 from ray.rllib.algorithms.algorithm import Algorithm
-
+from ray.air import session
 
 from torch_action_mask_model import TorchActionMaskModel
 from wrappers import OneHotShieldingWrapper, MiniGridShieldingWrapper
@@ -25,11 +25,13 @@ def shielding_env_creater(config):
     name = config.get("name", "MiniGrid-LavaCrossingS9N3-v0")
     framestack = config.get("framestack", 4)
     args = config.get("args", None)
-    args.grid_path = F"{args.grid_path}_{config.worker_index}.txt"
-    args.prism_path = F"{args.prism_path}_{config.worker_index}.prism"
-    
+    args.grid_path = F"{args.expname}_{args.grid_path}_{config.worker_index}.txt"
+    args.prism_path = F"{args.expname}_{args.prism_path}_{config.worker_index}.prism"   
     shielding = config.get("shielding", False)   
-    shield_creator = MiniGridShieldHandler(args.grid_path, args.grid_to_prism_binary_path, args.prism_path, args.formula)
+    shield_creator = MiniGridShieldHandler(grid_file=args.grid_path, 
+                                           grid_to_prism_path=args.grid_to_prism_binary_path,
+                                           prism_path=args.prism_path,
+                                           formula=args.formula)
     
     env = gym.make(name)
     env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query ,mask_actions=shielding)
@@ -64,7 +66,10 @@ def ppo(args):
         .rollouts(num_rollout_workers=args.workers)
         .resources(num_gpus=0)
         .environment( env="mini-grid-shielding",
-                      env_config={"name": args.env, "args": args, "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Training})
+                      env_config={"name": args.env,
+                                  "args": args,                                  
+                                  "shielding": args.shielding is ShieldingConfig.Full or args.shielding is ShieldingConfig.Training,
+                                  },)
         .framework("torch")
         .callbacks(MyCallbacks)
         .evaluation(evaluation_config={ 
@@ -72,7 +77,9 @@ def ppo(args):
                                         "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_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, 
@@ -87,7 +94,8 @@ def ppo(args):
                            metric="episode_reward_mean",
                            mode="max",
                            num_samples=1,
-                           trial_name_creator=trial_name_creator,                           
+                           trial_name_creator=trial_name_creator,
+
                        ),
                         run_config=air.RunConfig(
                                 stop = {"episode_reward_mean": 94,
@@ -144,7 +152,7 @@ def ppo(args):
         
     
 def main():
-    ray.init(num_cpus=4)
+    ray.init(num_cpus=3)
     import argparse
     args = parse_arguments(argparse)