论文标题
雪崩RL：持续的加固学习库
Avalanche RL: a Continual Reinforcement Learning Library
论文作者
论文摘要
持续的强化学习（CRL）是一个充满挑战的环境，代理商学会与随着时间的流逝不断变化的环境（经验流）进行互动。在本文中，我们描述了雪崩RL，这是一个用于连续加强学习的库，它允许在连续的任务流中轻松训练代理。 Avalanche RL基于Pytorch，并支持任何OpenAI健身环境。它的设计基于雪崩，这是最受欢迎的持续学习库之一，它使我们能够重复使用大量的持续学习策略，并改善强化学习与持续学习研究人员之间的相互作用。此外，我们提出了连续的栖息地，一种新颖的基准和高级文库，该图书馆可用于CRL研究的感性模拟器栖息地SIM。总体而言，雪崩RL试图在一个共同的框架持续的强化学习应用下统一，我们希望这将促进该领域的增长。
Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experiences). In this paper, we describe Avalanche RL, a library for Continual Reinforcement Learning which allows to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch and supports any OpenAI Gym environment. Its design is based on Avalanche, one of the more popular continual learning libraries, which allow us to reuse a large number of continual learning strategies and improve the interaction between reinforcement learning and continual learning researchers. Additionally, we propose Continual Habitat-Lab, a novel benchmark and a high-level library which enables the usage of the photorealistic simulator Habitat-Sim for CRL research. Overall, Avalanche RL attempts to unify under a common framework continual reinforcement learning applications, which we hope will foster the growth of the field.
