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):

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

pip install tensoraerospace

Save & share your models

All major agents support saving locally and pushing to the Hub:

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