Avalanche RL: a Continual Reinforcement Learning Library
This provides a unified framework and tools for researchers in continual reinforcement learning, though it is incremental as it builds on existing libraries like Avalanche.
The authors tackled the challenge of Continual Reinforcement Learning (CRL) by developing Avalanche RL, a library that enables training agents on continuous task streams, and introduced Continual Habitat-Lab as a new benchmark using photorealistic simulation.
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.