rlate-env 0.0.10

A collection of RL environments

RL Enviros

RL Enviros is a collection of custom environments compatible with Gymnasium, designed to simulate various games and scenarios for reinforcement learning experiments. This project aims to provide a variety of environments to help researchers and enthusiasts develop and test reinforcement learning algorithms.

Table of Contents

• Installation
• Environments
  • PickHigh
  • AnotherEnv
• Usage
• Contributing
• License

Installation

PyPi

You can install the rlate-env package directly from PyPi:

pip install rlate-env

Environments

PickHigh

PickHigh is a simple game where the player picks between two cards, aiming to select the higher card. This environment is useful for testing basic reinforcement learning algorithms.

• Observation Space: Discrete space of size 100, representing two cards (e.g., 34 for left card 3 and right card 4).
• Action Space: Discrete space of size 2. Action 0 selects the left card, and action 1 selects the right card.
• Reward:
  • +1 if the chosen card is higher.
  • 0 if both cards are the same.
  • -1 if the chosen card is lower.
• Episode Termination: The episode terminates when the player picks a card that is different from the dealer's card.

Detailed Documentation for PickHigh

Usage

Here is an example of how to use the PickHigh environment:

import gymnasium_rlate as rlate

# Create the Canon environment
env = rlate.Cannon()

# Reset the environment
obs, info = env.reset()
print(obs)

# Make a step in the environment
obs, reward, terminated, truncated, _ = env.step(23.5)
print(obs, reward, terminated)


# Render the environment
print(env.render())

# Close the environment
env.close()

Contributing

Contributions are welcome! If you have an environment you'd like to add or an improvement to suggest, please open an issue or submit a pull request.

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Open a pull request.

License

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

Repository

GitHub Repository: https://github.com/RLate-Space/RL-Enviros