论文标题
模仿：清洁模仿学习实现
imitation: Clean Imitation Learning Implementations
论文作者
论文摘要
模仿提供了pytorch中模仿和奖励学习算法的开源实现。我们包括三种逆增强学习（IRL）算法，三种模仿学习算法和偏好比较算法。该实现已根据先前的结果进行了基准测试，并且自动化测试涵盖了98％的代码。此外，这些算法是以模块化的方式实现的，使得在框架中开发新颖算法变得简单。我们的源代码，包括文档和示例，可在https://github.com/humancompatibleai/imitation上获得。
imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imitation learning algorithms and a preference comparison algorithm. The implementations have been benchmarked against previous results, and automated tests cover 98% of the code. Moreover, the algorithms are implemented in a modular fashion, making it simple to develop novel algorithms in the framework. Our source code, including documentation and examples, is available at https://github.com/HumanCompatibleAI/imitation
