RL Shooter is a Python-based framework that integrates ViZDoom with OpenAI Gym APIs to create a flexible reinforcement learning environment for FPS games. Users can define custom scenarios, maps, and reward structures to train agents on navigation, target detection, and shooting tasks. With configurable observation frames, action spaces, and logging facilities, it supports popular deep RL libraries such as Stable Baselines and RLlib, enabling clear performance tracking and reproducibility across experiments.

Simple Playgrounds provides a modular platform for building interactive 2D grid environments where agents can navigate mazes, interact with objects, and complete tasks. Users define environment layouts, object behaviors, and reward functions via simple YAML or Python scripts. The integrated Pygame renderer delivers real-time visualization, while a step-based API ensures seamless integration with reinforcement learning libraries like Stable Baselines3. With support for multi-agent setups, collision detection, and customizable physics parameters, Simple Playgrounds streamlines the prototyping, benchmarking, and educational demonstration of AI algorithms.

Gym-Recsys is a toolbox that wraps recommendation tasks into OpenAI Gym environments, allowing reinforcement learning algorithms to interact with simulated user-item matrices step by step. It provides synthetic user behavior generators, supports loading popular datasets, and delivers standard recommendation metrics like Precision@K and NDCG. Users can customize reward functions, user models, and item pools to experiment with different RL-based recommendation strategies in a reproducible manner.

gym-fx is an open-source Python library that implements a simulated Forex trading environment using the OpenAI Gym interface. It offers support for multiple currency pairs, integration of historical price feeds, technical indicators, and fully customizable reward functions. By providing a standardized API, gym-fx simplifies the process of benchmarking and developing reinforcement learning algorithms for algorithmic trading. Users can configure market slippage, transaction costs, and observation spaces to closely mimic live trading scenarios, facilitating robust strategy development and evaluation.