  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.
