target-gym 0.3.1
A reinforcement learning environment for simulating target-MDP environments (Airplane, Car, Bicycle), with gymnasium and gymnax support
Requires Python: >=3.10
Keywords: reinforcement-learning, gymnasium, jax, airplane, car, simulation, bike

# 🎯 TargetGym: Reinforcement Learning Environments for Target MDPs



**TargetGym** is a lightweight yet realistic collection of **reinforcement learning environments** designed around **target MDPs** — tasks where the objective is to **reach and maintain a specific subset of states** (target states).

Environments are built to be **fast, parallelizable, and physics-based**, enabling large-scale RL research while capturing the core challenges of real-world control systems such as **delays, irrecoverable states, partial observability, and competing objectives**.

Currently included environments:

* 🛩 **Plane** – control of a 2D Airbus A320-like aircraft - Stable-Target-MDP
* 🚗 **Car** – maintain a desired speed on a road - Stable-Target-MDP
* 🚲 **Bike** – stabilize and steer a 2D bicycle model - Unstable-Target-MDP (from [Randlov et al.](https://gwern.net/doc/reinforcement-learning/model-free/1998-randlov.pdf))

As well as environments adapted from [Process-Control Gym](https://github.com/MaximilianB2/pc-gym) (with gymnax support for faster speeds):
* 🧪 **CSTR** - control of a chemical reaction in a continuous stirred-tank reactor (CSTR).
* **More to come**

| Environment | Target Type         | Action Dim | State Dim | Steps / Second (1 env - $10^8$ steps - CPU) |
|-------------|---------------------|------------|-----------|-----------------|
| 🛩 Plane    | Stable-Target-MDP   | 2 (power, stick) | 9 (z, ż, ẋ, θ, θ̇, …) | ~0.54M |
| 🚗 Car      | Stable-Target-MDP   | 1 (throttle/brake) | 12 (velocity, lidar, …) | ~0.85M |
| 🚲 Bike     | Unstable-Target-MDP | 2 (torque, displacement) | 5 (angle, angular vel, pos, …) | ~1.77M |
| 🧪 CSTR     | Stable-Target-MDP   | 1 (coolant temperature) | 3 (concentration, temperature, target temperature) | ~1.49M |

<table align="center">
  <tr>
    <td align="center">
      <img src="videos/plane/output.gif" width="300px"/><br/>
      Plane
    </td>
    <td align="center">
      <img src="videos/car/output.gif" width="300px"/><br/>
      Car
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="videos/bike/output.gif" width="300px"/><br/>
      Bike
    </td>
    <td align="center">
      <img src="videos/cstr/output.gif" width="300px"/><br/>
      CSTR
    </td>
  </tr>
</table>



---

## Features

* **Fast & parallelizable** with JAX — scale to thousands of parallel environments on GPU/TPU.
* **Physics-based**: Derived from modeling equations, not arcade physics.
* **Reliable**: Unit-tested for stability and reproducibility.
* **Target MDP focus**: Each task is about reaching and maintaining target states.
* **Challenging dynamics**: Captures irrecoverable states, and momentum effects.
* **Visualization**: All environments come with visualization.
* **Compatible with RL libraries**: Offers [Gymnax](https://github.com/RobertTLange/gymnax) and [Gymnasium](https://github.com/Farama-Foundation/Gymnasium) interfaces.
* 📦 **Upcoming features**: Environmental perturbations (wind, turbulence, bumpy road) and fuel consumption.

---

## Stability

<!-- 
Below is an example of how stable altitude changes with engine power and pitch in the **Plane** environment:

<p align="center">
  <img src="figures/plane/3d_altitude.png" width="70%"/>
</p> -->

TargetGym offers a variety of **stable-target-MDPs**. This can be seen in the evolution of the target variable under constant policies for all environments:

<table align="center">
  <tr>
    <td align="center">
      <img src="figures/plane/power_trajectories.png" width="80%"/><br/>
      Plane
    </td>
    <td align="center">
      <img src="figures/bike/power_trajectories.png" width="80%"/><br/>
      Bike
    </td>
  </tr>
  <tr>
    <td align="center">
      <img src="figures/car/throttle_trajectories.png" width="80%"/><br/>
      Car
    </td>
    <td align="center">
      <img src="figures/cstr/trajectories.png" width="80%"/><br/>
      CSTR
    </td>
  </tr>
</table>

Some environments like Plane and Bike offer 2D-actions, for example power and stick for Plane:

<p align="center">
  <img src="figures/plane/3d_altitude.png" width="70%"/>
</p> 

<!-- 
This illustrates **multi-stability**: with fixed power and pitch, the aircraft naturally converges to a stable altitude. Similar properties can be found in Car environment -->

---

## Installation

Once released on PyPI, install with:

```bash
# Using pip
pip install target-gym

# Or with Poetry
poetry add target-gym
```

---

## Usage

Here’s a minimal example of running an episode in the **Plane** environment and saving a video:

```python
from target_gym import Plane, PlaneParams

# Create env
env = Plane()
seed = 42
env_params = PlaneParams(max_steps_in_episode=1_000)

# Simple constant policy with 80% power and 0° stick input
action = (0.8, 0.0)

# Save the video
env.save_video(lambda o: action, seed, folder="videos", episode_index=0, params=env_params, format="gif")
```

Or train an agent using your favorite RL library (example with stable-baselines3):

```python
from target_gym import GymnasiumPlane
from stable_baselines3 import SAC

env = GymnasiumPlane()
model = SAC("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=10_000, log_interval=4)
model.save("sac_plane")

obs, info = env.reset()
while True:
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        break
```

---

## Challenges Modeled

TargetGym tasks are designed to expose RL agents to **realistic control challenges**:

* [x] **Delays**: Inputs (like engine power) take time to fully apply.
* [x] **Partial observability**: Some parts of the state cannot be directly measured.
* [x] **Competing objectives**: Reach the target state quickly while minimizing overshoot or cost.
* [x] **Momentum effects**: Physical inertia delays control effectiveness.
* [x] **Irrecoverable states**: Certain trajectories inevitably lead to failure.
* [ ] **Non-stationarity**: Introduce perturbations in the environments.

---

## Roadmap

* [ ] Add perturbations (wind, turbulence, uneven terrain) for non-stationary dynamics.
* [ ] Easier interface for creating partially-observable variants.
* [ ] Provide benchmark results for popular RL baselines.
* [ ] Add fuel consumption and resource constraints.
* [ ] Add more PC-gym tasks.

---

## 🤝 Contributing

Contributions are welcome!
Open an issue or PR if you have suggestions, bug reports, or new features.

For development you need to install the dev dependencies, which include test, lint and agent dependencies. 

```bash
git clone https://github.com/YannBerthelot/TargetGym.git
cd TargetGym

# Using Poetry (recommended)
poetry install --with dev

# Using pip
python -m pip install -e ".[dev]"

```

---


## 📖 Citation

If you use **TargetGym** in your research or project, please cite it as:

```bibtex
@misc{targetgym2025,
  title        = {TargetGym: Reinforcement Learning Environments for Target MDPs},
  author       = {Yann Berthelot},
  year         = {2025},
  url          = {https://github.com/YannBerthelot/TargetGym},
  note         = {Lightweight physics-based RL environments for aircraft, car, and bike control}
}
```


---

## 📜 License

MIT License – free to use in research and projects.