♻️ crazy refactor

configs/training/ppo_real.yaml

@@ -0,0 +1,27 @@
+# PPO tuned for single-env real-time training on real hardware.
+# Inherits defaults + HPO ranges from ppo.yaml.
+# ~50 Hz control × 1 env = ~50 timesteps/s.
+# 100k timesteps ≈ 33 minutes of wall-clock training.
+
+defaults:
+  - ppo
+  - _self_
+
+hidden_sizes: [256, 256]
+total_timesteps: 100000
+learning_epochs: 5
+learning_rate: 0.001            # conservative — can't undo real-world damage
+entropy_loss_scale: 0.0001
+log_interval: 1024
+checkpoint_interval: 5000       # frequent saves — can't rewind real hardware
+initial_log_std: -0.5           # moderate initial exploration
+min_log_std: -4.0
+max_log_std: 0.0                # cap σ at 1.0
+
+# Never run real-hardware training remotely
+remote: false
+
+# Tighter HPO ranges for real hardware (override base ppo.yaml ranges)
+hpo:
+  entropy_loss_scale: {min: 0.00005, max: 0.001}
+  learning_rate: {min: 0.0003, max: 0.003}