platform-lander 0.1.9

Standalone reusable-booster landing environment for reinforcement learning.

Platform Lander

A standalone reusable-booster landing environment based on Gymnasium LunarLander v3 physics, but without importing Gymnasium. The task is to land a SpaceX-style booster upright on a moving floating platform. Missing the platform and falling into the ocean, or contacting the platform in a non-vertical position, terminates the episode as failure.

Install

After the package has been published to PyPI:

pip install platform_lander

Before the PyPI release is available, install the same package directly from the book repository subdirectory:

pip install "platform_lander @ git+https://github.com/aburkov/theDRLbook.git#subdirectory=test_environments/platform_lander"

For local development from this folder:

pip install -e .

Google Colab

Use the same install command in the first notebook cell. Colab usually needs swig before Box2D builds:

!apt-get -qq install swig
!pip install -q platform_lander

Then import normally:

from platform_lander import PlatformLander

env = PlatformLander(render_mode="rgb_array", enable_wind=True, wind_power=5.0)
obs, info = env.reset(seed=0)
obs, reward, terminated, truncated, info = env.step(2)
frame = env.render()

Display a rendered frame in Colab:

import matplotlib.pyplot as plt

plt.imshow(frame)
plt.axis("off")
plt.show()

Local Script

To watch the booster in a local Pygame window, install the package in editable mode and run the demo:

pip install -e .
python examples/demo.py

The test file is headless, so running pytest or python tests/test_platform_lander.py will not open an animation window.

To train a discrete policy with the textbook single-trajectory REINFORCE algorithm and then show three animated runs:

pip install -e ".[train]"
python vanilla_reinforce.py

The repository also includes incremental REINFORCE variants:

python rtg_reinforce.py                                  # vanilla + per-timestep reward-to-go
python average_reinforcement_baseline_reinforce.py       # reward-to-go + running scalar RTG baseline
python value_function_baseline_reinforce.py              # reward-to-go + learned value-function baseline
python batch_reinforce.py                                # vanilla + trajectory batches
python full_reinforce.py                                 # batches + reward-to-go + selectable scalar baseline

Each training script writes a log, per-episode CSV data, and a checkpoint under runs/ by default, for example runs/full_reinforce.log, runs/full_reinforce.csv, and runs/full_reinforce.pt. Override those paths with --log-file, --csv-file, and --model-file.

To load the hardcoded runs/full_reinforce.pt checkpoint and watch several animated policy rollouts:

python watch_trained_policy.py

To generate one side-by-side results graph per variant from the saved CSV files:

python plot_reinforce_results.py

For a quick smoke test without opening the animation window:

python vanilla_reinforce.py --episodes 3 --max-steps 20 --no-animation

Training scripts also expose reward-scale controls for experiments:

python gamma_dropped_rtg_reinforce.py --success-reward 500 --failure-reward -500 --shaping-factor 0.5

from platform_lander import PlatformLander

env = PlatformLander(enable_wind=True, wind_direction=(1, 0.2), wind_power=5.0)
obs, info = env.reset(seed=0)

for _ in range(1000):
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        print(info)
        break

env.close()

API Notes

• PlatformLander(continuous=False) uses Discrete(4) actions.
• Actions: 0 no-op, 1 upper-left attitude jet, 2 bottom engine, 3 upper-right attitude jet.
• continuous=True uses a two-value Box(-1, 1, shape=(2,)) action.
• Wind is controlled with enable_wind, wind_power, wind_direction, and set_wind(...).
• The platform moves horizontally at 1.15 / 3.0 world units per second by default.
• Terminal rewards default to success_reward=100.0 and failure_reward=-100.0.
• Dense shaping is multiplied by shaping_factor, which defaults to 1.0.
• Dense shaping rewards lateral alignment, low speed, vertical attitude, and foot contact; it does not reward reduced vertical distance to the platform.
• The booster has 50 available jet fires by default. After they are exhausted, engine commands have no effect and the booster continues ballistically.
• The observation includes the fraction of jet fires remaining.
• The package provides local Box and Discrete spaces and does not import Gymnasium.

Publishing

Build the package from this directory:

python -m build

Upload the generated dist/platform_lander-*.tar.gz and dist/platform_lander-*.whl files to PyPI with a PyPI account that owns the platform_lander project name:

python -m twine upload dist/*