The 2021 Google Research, Brain Team paper "Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning" introduces Policy Similarity Embeddings (PSEs), a novel framework designed to help reinforcement learning (RL) agents apply their skills to unfamiliar tasks. Traditional methods often struggle with generalization, failing when minor visual changes occur in semantically identical environments. To fix this, the researchers developed the Policy Similarity Metric (PSM), which identifies states as equivalent if they require the same optimal actions both now and in the future. By using contrastive metric embeddings, the system trains neural networks to group these behaviorally similar states together in a shared representation space. Experimental results on jumping tasks and complex control suites demonstrate that this approach significantly outperforms standard data augmentation and regularization techniques. Ultimately, the work proves that focusing on sequential behavioral patterns rather than just visual data allows agents to adapt much more effectively to new challenges. Source: September 29 2021 Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning Google Research, Brain Team Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare https://arxiv.org/pdf/2101.05265