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 users to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch [23] and supports any OpenAI Gym [4] environment. Its design is based on Avalanche [16], one of the most 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 [28] 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.

Avalanche RL: A Continual Reinforcement Learning Library

Carta A.;Lomonaco V.;Bacciu D.
2022-01-01

Abstract

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 users to easily train agents on a continuous stream of tasks. Avalanche RL is based on PyTorch [23] and supports any OpenAI Gym [4] environment. Its design is based on Avalanche [16], one of the most 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 [28] 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.
2022
978-3-031-06426-5
978-3-031-06427-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1149642
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