विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| प्रबलन शिक्षण के साथ स्थानांतरण शिक्षण× | पुनर्बलन अधिगम× | |
|---|---|---|
| क्षेत्र | गहन अधिगम | गहन अधिगम |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2009 (survey); concept from early 2000s | 1950s–1998 |
| प्रवर्तक≠ | Taylor, M. E. & Stone, P. | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) |
| प्रकार≠ | Transfer learning paradigm for sequential decision-making | Sequential decision-making framework |
| मौलिक स्रोत≠ | Taylor, M. E., & Stone, P. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research, 10, 1633–1685. link ↗ | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 |
| उपनाम | Transfer RL, TL for RL, cross-task reinforcement learning, inductive transfer in RL | RL, reward-based learning, trial-and-error learning, policy optimization |
| संबंधित≠ | 4 | 2 |
| सारांश≠ | Transfer Learning with Reinforcement Learning (Transfer RL) is a training paradigm in which knowledge acquired by an agent in one or more source tasks — encoded as policy weights, value functions, or learned representations — is reused to accelerate or improve learning in a related but different target task. It directly addresses the sample-inefficiency that plagues reinforcement learning from scratch in complex or expensive environments. | Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions by interacting with an environment, receiving scalar reward signals, and updating a policy to maximise cumulative future reward. Unlike supervised learning, no labeled examples are provided; the agent discovers optimal behavior entirely through experience and delayed feedback. |
| ScholarGateडेटासेट ↗ |
|
|