Search Results for "source testing unit testing"

HighwayEnv is an OpenAI Gym-compatible environment focused on autonomous driving scenarios. It provides flexible simulations for testing decision-making algorithms in highway, intersection, and merging traffic situations.

gym-anytrading is an OpenAI Gym-compatible environment designed for developing and testing reinforcement learning algorithms on trading strategies. It simulates trading environments for financial markets, including stocks and forex.

CORL (Collection of Reinforcement Learning Environments for Control Tasks) is a modular and extensible set of high-quality reinforcement learning environments focused on continuous control and robotics. It aims to offer standardized environments suitable for benchmarking state-of-the-art RL algorithms in control tasks, including physics-based simulations and custom-designed scenarios.

Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments...

This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.