SCORE: Simple Contrastive Representation and Reset-Ensemble for offline meta-reinforcement learning

Offline meta-reinforcement learning (OMRL) aims to train agents to quickly adapt to new tasks using only pre-collected data. However, existing OMRL methods often involve numerous ineffective training iterations and may experience performance collapse in the later stages of training. We identify the root cause-shallow memorization problem, where agents overspecialize in specific solutions for encountered states, hindering their generalization performance. This issue arises due to the loss of plasticity and the premature fitting of neural networks, which restricts the exploration of the agents. To address this challenge, we propose S imple CO ntrastive Representation and R eset-Ensemble for OMRL (SCORE), a novel context-based OMRL approach. SCORE introduces an end-to-end contrastive learning framework without negative samples to pre-train a context encoder, enabling more robust task representations. Subsequently, the context encoder is fine-tuned during meta-training. Furthermore, SCORE employs a Reset-Ensemble mechanism that periodically resets and ensembles partial networks to maintain the agents' continual learning ability and enhance their perception of characteristics across diverse tasks. Extensive experiments demonstrate that our SCORE method effectively avoids premature fitting and exhibits excellent generalization performance.