---
language: en
library_name: tensoraerospace
license: mit
tags:
- reinforcement-learning
- control
- ihdp
- aerospace
- f16
- gymnasium
- tensorflow
- keras
pipeline_tag: reinforcement-learning
model-index:
- name: IHDP-F16 (TensorAeroSpace)
  results:
  - task:
      type: reinforcement-learning
      name: F-16 longitudinal alpha tracking
    dataset:
      name: Synthetic 5° step reference
      type: simulation
    metrics:
    - name: MAE (alpha)
      type: mae
      value: 0.042348
      unit: rad
    - name: RMSE (alpha)
      type: rmse
      value: 0.069442
      unit: rad
    - name: Max error (alpha)
      type: max_error
      value: 0.204428
      unit: rad
    - name: Settling time (95%)
      type: settling_time
      value: 2.87
      unit: s
---

# IHDP Agent for F-16 Longitudinal Control (TensorAeroSpace)

This repository contains an Incremental Heuristic Dynamic Programming (IHDP) agent trained for longitudinal control of the F-16, implemented with TensorAeroSpace.

The agent tracks a step reference in angle of attack (alpha). It uses two neural networks (Actor and Critic) and an online incremental model to adapt features during rollout.

## Model architecture and files

Saved artifacts (see this repository files):

- `config.json`: environment and agent configuration (IO, actor/critic/incremental settings, optional reference signal snapshot)
- `actor.h5`: Keras weights for the Actor network
- `critic.h5`: Keras weights for the Critic network
- `incremental_model/`:
  - `F.npy`, `G.npy`: incremental model matrices
  - `delta_xt.npy`, `delta_ut.npy` (if available): last gradients windows used during identification

## How to use

```python
import os
import numpy as np
import gymnasium as gym
from tensoraerospace.agent.ihdp.model import IHDPAgent

# Load pretrained IHDP agent (either from local folder or HF Hub repo ID)
repo_id = "<username>/<repo_name>"  # or path to a local saved folder
agent = IHDPAgent.from_pretrained(repo_id, access_token=os.getenv("HF_TOKEN"))

# Create F-16 longitudinal environment (must match config.json IO/params)
env = gym.make(
    "LinearLongitudinalF16-v0",
    number_time_steps=2002,
    initial_state=[[0], [0], [0]],
    reference_signal=np.zeros((1, 2002)),  # replace with your reference
    use_reward=False,
    state_space=["theta", "alpha", "q"],
    output_space=["theta", "alpha", "q"],
    control_space=["ele"],
    tracking_states=["alpha"],
)

# Example rollout (reference must be shaped [1, T])
obs, info = env.reset()
reference = env.unwrapped.reference_signal
for t in range(reference.shape[1] - 3):
    u = agent.predict(obs, reference, t)
    obs, r, terminated, truncated, info = env.step(np.array(u))
    if terminated or truncated:
        break
```

## Reproduce the training/evaluation

This model card is based on the notebook `example/general_examples/example_ihdp_beautiful.ipynb` with the following settings:

- Simulation time: 20 s, dt: 0.01 s → 2002 steps
- Tracking variable: alpha (angle of attack)
- Reference: step of 5° (converted to radians)
- Initial state: `[theta=0, alpha=0, q=0]`

## Results

From the evaluation run in the notebook:

- MAE (alpha): 0.042348 rad (2.426°)
- RMSE (alpha): 0.069442 rad (3.979°)
- Max error (alpha): 0.204428 rad (11.713°)
- Settling time (95%): 2.87 s

Control (elevator) statistics depend on limits and units used in the environment. Ensure your environment configuration matches `config.json`.

## Intended use and limitations

- Intended for research and educational use on aircraft longitudinal control tasks.
- This is a simulation-trained controller; it is not validated for real-world flight.
- Performance is sensitive to environment configuration and reference signals. Always align your env settings with the provided config.

## How this repository was created

Artifacts were saved using `IHDPAgent.save(...)`/`save_pretrained(...)` and uploaded with `IHDPAgent.push_to_hub(...)` in TensorAeroSpace.

## Citation

If you use this work, please cite TensorAeroSpace.

```
@software{TensorAeroSpace,
  title        = {TensorAeroSpace: Open source deep learning framework for aerospace objects},
  author       = {Mazaev, Artemiy and Davydov, Vasily and Li, Yakov},
  year         = {2025},
  url          = {https://github.com/mr8bit/TensorAeroSpace},
}
```