Cross Domain Reinforcement Learning

Cross-domain reinforcement learning (RL) aims to improve the efficiency and generalizability of RL agents by transferring knowledge learned in one environment or task to another, significantly reducing the need for extensive retraining. Current research focuses on developing methods to effectively transfer knowledge across domains with differing observation and action spaces, often employing techniques like representation alignment, similarity-based knowledge transfer, and pre-training with prototypes to bridge these gaps. This research is crucial for advancing RL applications in robotics and other fields where data collection is expensive or difficult, enabling the development of more robust and adaptable intelligent systems.