<ul><li>Deep Reinforcement Learning (RL) models often fail to generalize when changes occur in the environment's observations or task requirements.</li><li>This paper proposes a zero-shot method for mapping between latent spaces across different agents trained on different visual and task variations.</li><li>The approach learns a transformation that maps embeddings from one agent's encoder to another agent's encoder without further fine-tuning.</li><li>The framework preserves high performance under visual and task domain shifts, allowing for more robust reinforcement learning in dynamically changing environments.</li></ul>

Mapping representations in Reinforcement Learning via Semantic Alignment for Zero-Shot Stitching

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