reaver | Modular Deep Reinforcement Learning Framework | Reinforcement Learning library

reaver is a Python library typically used in Artificial Intelligence, Reinforcement Learning, Deep Learning, Pytorch, Tensorflow applications. reaver has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install reaver' or download it from GitHub, PyPI.

Reaver is a modular deep reinforcement learning framework with a focus on various StarCraft II based tasks, following in DeepMind's footsteps who are pushing state-of-the-art of the field through the lens of playing a modern video game with human-like interface and limitations. This includes observing visual features similar (though not identical) to what a human player would perceive and choosing actions from similar pool of options a human player would have. See StarCraft II: A New Challenge for Reinforcement Learning article for more details. Though development is research-driven, the philosophy behind Reaver API is akin to StarCraft II game itself - it has something to offer both for novices and experts in the field. For hobbyist programmers Reaver offers all the tools necessary to train DRL agents by modifying only a small and isolated part of the agent (e.g. hyperparameters). For veteran researchers Reaver offers simple, but performance-optimized codebase with modular architecture: agent, model, and environment are decoupled and can be swapped at will. While the focus of Reaver is on StarCraft II, it also has full support for other popular environments, notably Atari and MuJoCo. Reaver agent algorithms are validated against reference results, e.g. PPO agent is able to match Proximal Policy Optimization Algorithms. Please see below for more details.