Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| Apprentissage par renforcement faiblement supervisé× | Aprenentatge per Reforçament× | |
|---|---|---|
| Camp | Aprenentatge profund | Aprenentatge profund |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2010s–present | 1950s–1998 |
| Autor original≠ | Multiple contributors; reward-learning framing: Christiano et al. (2017) | Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations) |
| Tipus≠ | Reinforcement learning with imperfect or partial reward supervision | Sequential decision-making framework |
| Font seminal | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 | Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6 |
| Àlies | WSRL, weak-reward RL, imperfect-reward reinforcement learning, reward-impoverished RL | RL, reward-based learning, trial-and-error learning, policy optimization |
| Relacionats≠ | 3 | 2 |
| Resum≠ | Weakly supervised reinforcement learning (WSRL) trains agents in environments where the reward signal is imperfect, sparse, delayed, or only partially informative — unlike dense fully-supervised RL. The agent must learn effective policies despite incomplete feedback, using auxiliary signals, reward modeling, or preference learning to compensate for the weak supervision. | 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. |
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