✨ add new ppo mjx config

configs/training/ppo_mjx.yaml

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+# PPO tuned for MJX (1024+ parallel envs on GPU).
+# With 1024 envs, each timestep collects 1024 samples, so total_timesteps
+# can be much lower than the CPU config.
+
+hidden_sizes: [128, 128]
+total_timesteps: 300000       # 300K × 1024 envs ≈ 307M env steps
+rollout_steps: 1024           # PPO batch = 1024 envs × 1024 steps = 1M samples
+learning_epochs: 4
+mini_batches: 32              # keep mini-batch size similar to CPU config (~32K)
+discount_factor: 0.99
+gae_lambda: 0.95
+learning_rate: 0.001          # ~3x higher LR for 16x larger batch (sqrt scaling)
+clip_ratio: 0.2
+value_loss_scale: 0.5
+entropy_loss_scale: 0.05
+log_interval: 100             # log more often (shorter run)
+checkpoint_interval: 10000
+
+record_video_every: 10000
+
+# ClearML remote execution (GPU worker)
+remote: false