fix: resume from checkpoint log dir

sheeprl/algos/dreamer_v1/dreamer_v1.py

@@ -384,7 +384,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/dreamer_v2/dreamer_v2.py

@@ -404,7 +404,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/dreamer_v3/dreamer_v3.py

@@ -358,7 +358,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // fabric.world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/droq/droq.py

@@ -150,7 +150,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // fabric.world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/p2e_dv1/p2e_dv1.py

@@ -389,7 +389,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/p2e_dv2/p2e_dv2.py

@@ -491,7 +491,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/ppo/ppo.py

@@ -142,7 +142,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // fabric.world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/ppo/ppo_decoupled.py

@@ -56,7 +56,7 @@ def player(cfg: DictConfig, world_collective: TorchCollective, player_trainer_co
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // (world_collective.world_size - 1)
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Initialize logger
     root_dir = (

sheeprl/algos/ppo_recurrent/ppo_recurrent.py

@@ -137,7 +137,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // fabric.world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/sac/sac.py

@@ -105,7 +105,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // fabric.world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process

sheeprl/algos/sac/sac_decoupled.py

@@ -56,7 +56,7 @@ def player(cfg: DictConfig, world_collective: TorchCollective, player_trainer_co
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // fabric.world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Initialize logger
     root_dir = (

sheeprl/algos/sac_ae/sac_ae.py

@@ -185,7 +185,7 @@ def main(cfg: DictConfig):
         cfg.checkpoint.resume_from = str(ckpt_path)
         cfg.per_rank_batch_size = state["batch_size"] // fabric.world_size
         cfg.root_dir = root_dir
-        cfg.run_name = f"resume_from_checkpoint_{run_name}"
+        cfg.run_name = run_name
 
     # Create TensorBoardLogger. This will create the logger only on the
     # rank-0 process