Update README.md

README.md

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 ---
-title: README
-emoji: 🏢
-colorFrom: green
-colorTo: purple
-sdk: static
-pinned: false
+language: en
+license: mit
+library_name: tensoraerospace
+tags:
+- aerospace
+- reinforcement-learning
+- control
+- gymnasium
+- tensorflow
+- pytorch
 ---
 
-Edit this `README.md` markdown file to author your organization card.
+# TensorAeroSpace — Aerospace RL & Control Organization
+
+Realistic aerospace environments and modern RL/control algorithms for training flight control systems. Open‑source, MIT‑licensed.
+
+- Website & Docs: https://tensoraerospace.readthedocs.io/
+- GitHub: https://github.com/TensorAeroSpace/TensorAeroSpace
+- PyPI: https://pypi.org/project/tensoraerospace/
+
+## What we build
+
+- Environments: F‑16, B747, X‑15, rockets, satellites (state‑space models, linear/linearized)
+- Algorithms: IHDP, DQN, A3C/A2C‑NARX, PPO, SAC, DDPG, GAIL, PID, MPC
+- Tooling: benchmarking, metrics, examples, docs, and lessons
+
+## Featured model(s)
+
+- IHDP agent for F‑16 longitudinal alpha tracking — TensorAeroSpace/ihdp‑f16
+
+Quick use (IHDP):
+
+```python
+import os
+import numpy as np
+import gymnasium as gym
+from tensoraerospace.agent.ihdp.model import IHDPAgent
+
+agent = IHDPAgent.from_pretrained(
+    "TensorAeroSpace/ihdp-f16", access_token=os.getenv("HF_TOKEN")
+)
+
+env = gym.make(
+    "LinearLongitudinalF16-v0",
+    number_time_steps=2002,
+    initial_state=[[0],[0],[0]],
+    reference_signal=np.zeros((1, 2002)),
+    use_reward=False,
+    state_space=["theta","alpha","q"],
+    output_space=["theta","alpha","q"],
+    control_space=["ele"],
+    tracking_states=["alpha"],
+)
+
+obs, info = env.reset()
+ref = env.unwrapped.reference_signal
+for t in range(ref.shape[1]-3):
+    u = agent.predict(obs, ref, t)
+    obs, r, terminated, truncated, info = env.step(np.array(u))
+    if terminated or truncated:
+        break
+```
+
+## Install
+
+```bash
+pip install tensoraerospace
+```
+
+## Save & share your models
+
+All major agents support saving locally and pushing to the Hub:
+
+```python
+from tensoraerospace.agent.sac.sac import SAC
+
+agent = SAC(env)
+agent.train(num_episodes=1)
+
+# Save + push to Hub
+folder = agent.save_pretrained("./checkpoints")
+agent.push_to_hub("<org>/<model-name>", base_dir="./checkpoints", access_token="hf_...")
+```
+
+## Learn more
+
+- Quick start & examples: ./example/
+- English docs home: ./docs/en/index.md
+- Russian docs home: ./docs/ru/index.md
+
+## License
+
+MIT — free for academia and industry.
+
+---
+
+Source: TensorAeroSpace team org page on the Hub — https://huggingface.co/TensorAeroSpace
+
+