Reworked training

new reward/penalty system
learning phases with curriculum learning
new training parameters
cleanup of old code
better logging while training
multiple environments instead of robots (they could bumb into each other)

ml/run_train.py

@@ -1,7 +1,7 @@
 """Minimal training launcher for quick experiments.
 
 Usage:
-    python ml/run_train.py --timesteps 50000 --model ml/checkpoints/ppo_joint_command
+    python ml/run_train.py --timesteps 500000 --model ml/checkpoints/ppo_joint_command
 
 This is a convenience wrapper around `ml.train.train` with friendly defaults
 for interactive experimentation.
@@ -20,12 +20,12 @@ from ml.train import train
 
 def main():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--timesteps", type=int, default=50000, help="Total number of 'practice steps'.")
+    parser.add_argument("--timesteps", type=int, default=500000, help="Total number of 'practice steps'.")
     parser.add_argument("--model", type=str, default="ml/checkpoints/ppo_joint_command", help="Path to save the trained model")
     parser.add_argument("--seed", type=int, default=0, help="Random seed for reproducibility")
     parser.add_argument("--device", type=str, default="auto", help="Device to use: 'cpu', 'cuda', or 'auto'")
     parser.add_argument("--gui", action="store_true", help="Show the PyBullet GUI during training")
-    parser.add_argument("--num-workers", type=int, default=1, help="Number of training environment with one robot each")
+    parser.add_argument("--num-workers", type=int, default=1, help="Number of training environment workers")
     parser.add_argument("--robot-spacing", type=float, default=1.5, help="Spacing between robots in meters")
     parser.add_argument("--start-pose", type=str, choices=["init_deg", "init90_deg"], default="init_deg", help="Initial robot pose at reset")
     args = parser.parse_args()