gym-sailing: A sailing environment for OpenAI Gym / Gymnasium

This is a Gymnasium (OpenAI Gym) environment designed to train reinforcement learning (RL) agents to control a sailboat. The environment simulates the dynamics of a sailboat and allows the agent to learn tacking behavior to reach a target point.

Environments

Environment	Description
Sailboat-v0	The main environment with a continuous action space.
SailboatDiscrete-v0	A variation of the environment with a discrete action space.
Motorboat-v0	An easy test environment with a motorboat instead of a sailboat.

Installation

You can install the latest release using pip:

pip install gym-sailing

Alternatively, if you prefer, you can clone the repository and install it locally.

Usage

Basic Usage

Bare minimum code to run the environment:

import gymnasium as gym
import gym_sailing

env = gym.make("Sailboat-v0", render_mode="human")
observation, info = env.reset(seed=42)

for _ in range(1000):
    action = env.action_space.sample()  # this is where you would insert your policy
    observation, reward, terminated, truncated, info = env.step(action)

    if terminated or truncated:
        observation, info = env.reset()

env.close()

Training an RL Agent

To train an RL agent using stable-baselines3:

from stable_baselines3 import PPO
import gymnasium as gym
import gym_sailing

env = gym.make("Sailboat-v0")
model = PPO('MlpPolicy', env, verbose=1)

# Train the agent
model.learn(total_timesteps=1_000_000)

# Test the trained model
observation, info = env.reset()
for _ in range(1000):
    action, _ = model.predict(observation)
    observation, reward, terminated, truncated, info = env.step(action)

env.close()

Environment Details

Observation Space

The observation space includes:

• Boat Speed: The current speed of the boat.
• Boat Heading: The angle of the boat relative to the wind, ranging from -$\pi$ to $\pi$.
• Heading Rate: The rate of change of the boat's heading.
• Course to Target: The angle between the boat's heading and the target, ranging from -$\pi$ to $\pi$.
• Distance to Target: The normalized distance between the boat and the target.

Action Space

The action space consists of:

• Rudder Angle: The angle of the rudder, ranging from -1 to 1 for Sailboat-v0 and Motorboat-v0, and {-1, 0, 1} for SailboatDiscrete-v0.

Reward

The default reward function includes:

• Alive Penalty: A penalty for each time step to encourage the agent to reach the target quickly.
• Target Reward: A reward for reaching the target.
• Course Penalty: A penalty for leaving the course area.
• Progress Reward: A reward for making progress towards the target, using the L8 norm, to encourage the agent to move upwind.

Episode End

• The environment is terminated if the boat reaches the target or leaves the course area.
• The environment is truncated after 3000 steps.

Benchmarks

Benchmarks using stable-baselines3 with default hyperparameters. Good policies that tack only once tend to achieve ~390 total reward for the sailboat environment. PPO seems to perform better, but SAC is also a good option, even converging faster.

Contributing

Contributions are welcome. Please fork the repository and submit a pull request with your changes. For any questions or suggestions, feel free to open an issue.

Future Work

Here are some features I'd like to add in the future:

• Add currents of different intensities and directions.
• Add wind shifts.
• Add wind gusts and lulls.
• Make the polar diagram more accurate, using the data from this paper: R. Binns, F. W. Bethwaite, and N. R. Saunders, “Development of A More Realistic Sailing Simulator,” High Performance Yacht Design Conference. RINA, pp. 243–250, Dec. 04, 2002. doi: 10.3940/rina.ya.2002.29.

Inspiration

This project was inspired by this fork: https://github.com/openai/gym/compare/master...JonAsbury:gym:Sailing-Simulator-Env

License

This project is licensed under the MIT License - see the LICENSE file for details.