Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
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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
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