Knowledge Transfer in Dynamic Decision Making
Knowledge transfer is crucial for dynamic decision-making, particularly in today’s fast-paced environment where lifelong learning and explainability are essential. It enables individuals and systems to adapt quickly, leveraging past experiences to inform new decisions. This capability is increasingly important as we seek to generalize knowledge across various contexts, ensuring that insights are not only applicable but also easily understood.
In the realm of deep reinforcement learning (RL), this issue becomes even more pronounced. While deep RL has achieved superhuman performance in complex decision-making tasks, it often faces challenges with generalization and knowledge reuse—critical components of true intelligence. Addressing these challenges will enhance the efficacy of deep RL systems, allowing them to make informed decisions based on prior experiences, thereby pushing the boundaries of artificial intelligence.
Contact: Tatiana V. Guy, Siavash Fakhimi
One-to-one knowledge transfer
To enhance agent performance and reduce training time in RL, there is an urgent need for an efficient method for one-to-one knowledge transfer between different tasks. Transferred elements—whether skills or knowledge—are often specific to particular tasks and decision policies, which complicates their application. Therefore, it is crucial to concentrate on identifying and transferring the most informative decision patterns, ensuring that the skills remain relevant and effective.
We introduced a novel method for knowledge transfer between distinct RL tasks, leveraging an RL-specific modification of CycleGAN to enhance generalization and reduce training time. The proposed correspondence function identifies similarities between source and target tasks, ensuring efficient knowledge transfer. By adding two new components to the loss function, we extend the GAN and CycleGAN frameworks, demonstrating them as specific instances of our broader knowledge transfer solution in RL.
Contact: Marko Ruman, Tatiana V. Guy
Many-to-one knowledge transfer
In dynamic decision tasks, the need for N-to-1 knowledge transfer is critical for optimizing performance. As multiple tasks often share underlying patterns and strategies, effectively transferring knowledge from various sources to a single target task can significantly enhance learning efficiency and decision quality. This approach enables agents to leverage diverse experiences, improving their adaptability and performance in complex, evolving environments. Emphasizing N-to-1 knowledge transfer addresses the challenges of generalization and accelerates the training process, fostering more intelligent decision-making systems.
Contact: Marko Ruman, Tatiana V. Guy