chnaged logdir handling

examples/shields/rl/15_train_eval_tune.py

@@ -13,7 +13,7 @@ from ray.rllib.algorithms.algorithm import Algorithm
 
 from torch_action_mask_model import TorchActionMaskModel
 from wrappers import OneHotShieldingWrapper, MiniGridShieldingWrapper
-from helpers import parse_arguments, create_log_dir, ShieldingConfig
+from helpers import parse_arguments, create_log_dir, ShieldingConfig, test_name
 from shieldhandlers import MiniGridShieldHandler, create_shield_query
 
 from torch.utils.tensorboard import SummaryWriter
@@ -54,7 +54,7 @@ def register_minigrid_shielding_env(args):
 
 def ppo(args):
     register_minigrid_shielding_env(args)
-    logdir = create_log_dir(args)
+    logdir = args.log_dir
     
     config = (PPOConfig()
         .rollouts(num_rollout_workers=args.workers)
@@ -92,7 +92,9 @@ def ppo(args):
                                                                        checkpoint_score_attribute="episode_reward_mean",
                                                                        ),
                                 
-                               storage_path=F"{logdir}"
+                               storage_path=F"{logdir}",
+                               name=test_name(args)
+                               
     )
                         ,
     param_space=config,)
@@ -113,26 +115,26 @@ def ppo(args):
     algo = Algorithm.from_checkpoint(best_result.checkpoint)
     
        
-    eval_log_dir = F"{logdir}-eval"
+    # eval_log_dir = F"{logdir}-eval"
         
-    writer = SummaryWriter(log_dir=eval_log_dir)
-    csv_logger = CSVLogger(config=config, logdir=eval_log_dir)
+    # writer = SummaryWriter(log_dir=eval_log_dir)
+    # csv_logger = CSVLogger(config=config, logdir=eval_log_dir)
     
     
-    for i in range(args.evaluations):
-        eval_result = algo.evaluate()
-        print(pretty_print(eval_result))
-        print(eval_result)
-        # logger.on_result(eval_result)
+    # for i in range(args.evaluations):
+    #     eval_result = algo.evaluate()
+    #     print(pretty_print(eval_result))
+    #     print(eval_result)
+    #     # logger.on_result(eval_result)
 
-        csv_logger.on_result(eval_result)
+    #     csv_logger.on_result(eval_result)
         
-        evaluation = eval_result['evaluation']
-        epsiode_reward_mean = evaluation['episode_reward_mean']
-        episode_len_mean = evaluation['episode_len_mean']
-        print(epsiode_reward_mean)
-        writer.add_scalar("evaluation/episode_reward_mean", epsiode_reward_mean, i)
-        writer.add_scalar("evaluation/episode_len_mean", episode_len_mean, i)
+    #     evaluation = eval_result['evaluation']
+    #     epsiode_reward_mean = evaluation['episode_reward_mean']
+    #     episode_len_mean = evaluation['episode_len_mean']
+    #     print(epsiode_reward_mean)
+    #     writer.add_scalar("evaluation/episode_reward_mean", epsiode_reward_mean, i)
+    #     writer.add_scalar("evaluation/episode_len_mean", episode_len_mean, i)
         
     
 def main():

examples/shields/rl/helpers.py

@@ -39,8 +39,10 @@ def extract_keys(env):
     return keys
 
 def create_log_dir(args):
-    return F"{args.log_dir}{args.algorithm}-shielding:{args.shielding}-evaluations:{args.evaluations}-steps:{args.steps}-env:{args.env}"
+    return F"{args.log_dir}sh:{args.shielding}-env:{args.env}"
 
+def test_name(args):
+    return F"sh:{args.shielding}-env:{args.env}"
 
 def get_action_index_mapping(actions):
     for action_str in actions: