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README.md

@@ -12,7 +12,6 @@ tags:
 - HAMA
 - laser-machining
 - SAC
-- ml-intern
 pipeline_tag: reinforcement-learning
 ---
 # Femtosecond Laser Hydrogel Etching - RL-Optimized Model v2
@@ -57,23 +56,3 @@ Ensemble ML (XGBoost+NN) + SAC Reinforcement Learning for fs-laser hydrogel etch
 2. Shaping Laser Pulses with RL (arxiv:2503.00499)
 3. Recipe RL for Chemical Processes (arxiv:2511.16297)
 4. Petit et al., JLMN 2025 (XGBoost for fs-laser)
-
-<!-- ml-intern-provenance -->
-## Generated by ML Intern
-
-This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.
-
-- Try ML Intern: https://smolagents-ml-intern.hf.space
-- Source code: https://github.com/huggingface/ml-intern
-
-## Usage
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = 'TWLAb/femtosecond-laser-hydrogel-etching-model'
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id)
-```
-
-For non-causal architectures, replace `AutoModelForCausalLM` with the appropriate `AutoModel` class.

rl_results_v2.json

@@ -0,0 +1 @@
+{"success_rate": 100.0, "avg_rel_error": 0.10709077551960945, "avg_abs_error": 2.596579451560974, "timesteps": 20000, "gamma": 0.7, "algo": "SAC_v2", "focus": "depth+width"}

sac_optimizer_v2.zip

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