Added config saving on new training run

sim/algo/ppo/on_policy_runner.py

@@ -30,6 +30,7 @@
 # Copyright (c) 2024 Beijing RobotEra TECHNOLOGY CO.,LTD. All rights reserved.
 # type: ignore
 
+import json
 import os
 import statistics
 import time
@@ -46,6 +47,51 @@
 from sim.algo.vec_env import VecEnv
 
 
+def make_serializable(obj):
+    """Recursively convert an object and its components to JSON-serializable types."""
+    if isinstance(obj, dict):
+        return {key: make_serializable(value) for key, value in obj.items()}
+    elif isinstance(obj, (list, tuple)):
+        return [make_serializable(item) for item in obj]
+    try:
+        # Try standard JSON serialization first
+        json.dumps(obj)
+        return obj
+    except (TypeError, OverflowError):
+        try:
+            # for numpy types
+            return float(obj)
+        except:
+            try:
+                return int(obj)
+            except:
+                # If all else fails, convert to string representation
+                return f"<{type(obj).__name__}:{str(obj)}>"
+
+
+def write_config_file(config: dict, log_dir: str) -> None:
+    """Writes the configuration to a JSON file in the log directory."""
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+
+    metadata = {
+        "creation_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
+        "experiment_name": config["runner"]["experiment_name"],
+        "run_name": config["runner"]["run_name"],
+    }
+
+    serializable_config = make_serializable(config)
+
+    full_config = {
+        "metadata": metadata,
+        "configuration": serializable_config
+    }
+
+    json_path = os.path.join(log_dir, "experiment_config.json")
+    with open(json_path, 'w') as f:
+        json.dump(full_config, f, indent=2, sort_keys=True)
+
+
 class OnPolicyRunner:
     def __init__(self, env: VecEnv, train_cfg: dict, log_dir: Optional[str] = None, device: str = "cpu"):
         self.cfg = train_cfg["runner"]
@@ -102,6 +148,8 @@ def learn(self, num_learning_iterations: int, init_at_random_ep_len: bool = Fals
                 config=self.all_cfg,
             )
             self.writer = SummaryWriter(log_dir=self.log_dir, flush_secs=10)
+            if self.current_learning_iteration == 0:
+                write_config_file(self.all_cfg, self.log_dir)
         if init_at_random_ep_len:
             self.env.episode_length_buf = torch.randint_like(
                 self.env.episode_length_buf, high=int(self.env.max_episode_length)