<ul><li>Researchers have introduced HiSPO, a hierarchical framework for Continual Reinforcement Learning.</li><li>The framework addresses the challenge of avoiding forgetting previously acquired knowledge while adapting to new tasks in navigation settings.</li><li>HiSPO leverages distinct policy subspaces of neural networks for efficient adaptation and preservation of existing knowledge.</li><li>Experimental results show competitive performance and adaptability in both maze environments and video game-like navigation simulations.</li></ul>

Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning

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