v0.1.8

robogauge/utils/helpers.py

@@ -38,6 +38,21 @@ def class_to_dict(obj) -> dict:
         result[key] = element
     return result
 
+def set_seed(seed: int):
+    import os
+    import torch
+    import random
+    import numpy as np
+    assert seed >= 0, "Seed must be non-negative."
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(seed)
+        torch.cuda.manual_seed_all(seed)
+
 def str2bool(v):
     if v.lower() in ('yes', 'true', 't', 'y', '1'):
         return True
@@ -48,12 +63,21 @@ def str2bool(v):
 def parse_args():
     parser = ArgumentParser()
     parameters = [
+        # Single run parameters
         {"name": "--task-name", "type": str, "default": "base", "help": "Name of the task to run."},
         {"name": "--experiment-name", "type": str, "help": "Name of the experiment to run."},
-        {"name": "--run-name", "type": str, "default": "run1", "help": "Name of the run."},
+        {"name": "--run-name", "type": str, "default": "run", "help": "Name of the run."},
         {"name": "--model-path", "type": str, "help": "Path to the model file."},
         {"name": "--headless", "action": "store_true", "default": False, "help": "Run in headless mode."},
         {"name": "--save-video", "action": "store_true", "default": False, "help": "Save video output."},
+        {"name": "--seed", "type": int, "default": 42, "help": "Random seed."},
+
+        # Multiprocessing parameters, with different seeds
+        {"name": "--multi", "action": "store_true", "default": False, "help": "Enable multiprocessing."},
+        {"name": "--num-processes", "type": int, "default": 2, "help": "Number of parallel processes."},
+        {"name": "--seeds", "type": int, "nargs": "+", "default": [0], "help": "List of random seeds for multiple runs."},
+        {"name": "--base-masses", "type": float, "nargs": "+", "default": [-1, 0, 1], "help": "List of base masses for the model."},
+        {"name": "--frictions", "type": float, "nargs": "+", "default": [0.5, 1.0, 1.5], "help": "List of friction coefficients for the model."}
     ]
     for param in parameters:
         parser.add_argument(param['name'], **{k: v for k, v in param.items() if k != 'name'})

robogauge/utils/task_register.py

@@ -7,7 +7,9 @@
 @Blog    : https://wty-yy.github.io/
 @Desc    : Task Registration Utility
 '''
-from robogauge.utils.helpers import parse_args
+from robogauge import ROBOGAUGE_ROOT_DIR
+from robogauge.utils.logger import logger
+from robogauge.utils.helpers import parse_args, set_seed
 
 class TaskRegister():
     def __init__(self):
@@ -29,13 +31,13 @@ class TaskRegister():
     
     def get_cfgs(self, name):
         if name not in self.sim_cfgs:
-            raise ValueError(f"Task '{name}' is not registered.")
+            raise ValueError(f"Task '{name}' is not registered, checkout '{ROBOGAUGE_ROOT_DIR}/robogauge/tasks/__init__.py'.")
         sim_cfg = self.sim_cfgs[name]
         gauger_cfg = self.gauger_cfgs[name]
         robot_cfg = self.robot_cfgs[name]
         return sim_cfg, gauger_cfg, robot_cfg
     
-    def make_pipeline(self, args=None, sim_cfg=None, gauger_cfg=None, robot_cfg=None):
+    def make_pipeline(self, args=None, sim_cfg=None, gauger_cfg=None, robot_cfg=None, create_logger=True):
         if args is None:
             args = parse_args()
         default_cfgs = self.get_cfgs(args.task_name)
@@ -48,7 +50,11 @@ class TaskRegister():
         if args is not None:
             self.update_args_to_cfg(sim_cfg, gauger_cfg, robot_cfg, args)
         pipeline_class = self.get_pipeline_class(args.task_name)
-        return pipeline_class(args.run_name, sim_cfg, robot_cfg, gauger_cfg)
+        set_seed(args.seed)
+        run_name = args.run_name + f'_{args.seed}'
+        if create_logger:
+            logger.create(args.experiment_name, run_name)
+        return pipeline_class(run_name, sim_cfg, robot_cfg, gauger_cfg)
 
     def update_args_to_cfg(self, sim_cfg, gauger_cfg, robot_cfg, args):
         if args.model_path is not None: