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Effectively exploring the environment is a key challenge in reinforcementlearning (RL). We address this challenge by defining a novel intrinsic rewardbased on a foundation model, such as contrastive language image pretraining(CLIP), which can encode a wealth of domain-independent semanticvisual-language knowledge about the world. Specifically, our intrinsic rewardis defined based on pre-trained CLIP embeddings without any fine-tuning orlearning on the target RL task. We demonstrate that CLIP-based intrinsicrewards can drive exploration towards semantically meaningful states andoutperform state-of-the-art methods in challenging sparse-rewardprocedurally-generated environments.

Foundation Models for Semantic Novelty in Reinforcement Learning