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Simulation experiments demonstrate that LLIRL outperforms relevant existing methods and enables effective incremental adaptation to various dynamic environments for lifelong learning.Ī key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while minimizing the catastrophic forgetting of the learned information. This method allows for all environment models to be adapted as necessary, with new models instantiated for environmental changes and old models retrieved when previously seen environments are encountered again. In EM, the E-step involves estimating the posterior expectation of environment-to-cluster assignments, whereas the M-step updates the environment parameters for future learning.

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During lifelong learning, we employ the expectation-maximization (EM) algorithm with online Bayesian inference to update the mixture in a fully incremental manner. The prior distribution over the mixture is formulated as a Chinese restaurant process (CRP), which incrementally instantiates new environment models without any external information to signal environmental changes in advance. We develop and maintain a library that contains an infinite mixture of parameterized environment models, which is equivalent to clustering environment parameters in a latent space. In this article, we propose lifelong incremental reinforcement learning (LLIRL), a new incremental algorithm for efficient lifelong adaptation to dynamic environments. A central capability of a long-lived reinforcement learning (RL) agent is to incrementally adapt its behavior as its environment changes and to incrementally build upon previous experiences to facilitate future learning in real-world scenarios.

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