simulation physics fixed

fuck them physics

ml/callbacks.py

@@ -0,0 +1,44 @@
+"""
+ml/callbacks.py - Stable-Baselines3 Custom Callbacks for Logging & Curriculum Advancement
+"""
+import os
+import numpy as np
+from stable_baselines3.common.callbacks import BaseCallback
+
+
+class RewardLoggerCallback(BaseCallback):
+    """Logs individual reward component averages to TensorBoard."""
+
+    def __init__(self, verbose: int = 0):
+        super().__init__(verbose)
+
+    def _on_step(self) -> bool:
+        # Pull component averages from the environment vector
+        for env_idx, env in enumerate(self.training_env.envs):
+            if hasattr(env, "get_reward_component_averages"):
+                averages = env.get_reward_component_averages()
+                for key, val in averages.items():
+                    self.logger.record(f"reward_components/{key}", val)
+        return True
+
+
+class CurriculumCallback(BaseCallback):
+    """Monitors evaluation performance and automatically manages curriculum progression."""
+
+    def __init__(self, eval_freq: int = 10000, verbose: int = 1):
+        super().__init__(verbose)
+        self.eval_freq = eval_freq
+
+    def _on_step(self) -> bool:
+        if self.n_calls % self.eval_freq == 0:
+            for env in self.training_env.envs:
+                if hasattr(env, "get_current_robot_metrics"):
+                    metrics = env.get_current_robot_metrics()
+                    if metrics:
+                        phase = metrics[0].get("phase_name", "UNKNOWN")
+                        dist = metrics[0].get("distance_from_start", 0.0)
+                        self.logger.record("curriculum/phase_idx", phase)
+                        self.logger.record("curriculum/max_distance", dist)
+                        if self.verbose > 0:
+                            print(f"[CurriculumCallback] Step {self.num_timesteps}: Current Phase = {phase}, Max Dist = {dist:.2f}m")
+        return True