Comparar métodos

	Aprendizaje por Refuerzo	Métodos de Gradiente de Política
Campo	Aprendizaje profundo	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	1950s–1998	1992
Autor original	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)	Ronald Williams (REINFORCE); Sutton et al. (policy gradient theorem)
Tipo	Sequential decision-making framework	Policy-based reinforcement learning
Fuente seminal	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6	Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8(3–4), 229–256.
Alias	RL, reward-based learning, trial-and-error learning, policy optimization	REINFORCE, actor-critic, policy optimization, politika gradyanı
Relacionados	2	4
Resumen	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.	Policy gradient methods are reinforcement-learning algorithms that optimize a parameterized policy directly by gradient ascent on the expected return, rather than learning action-values and acting greedily. Founded on Ronald Williams' 1992 REINFORCE algorithm and the policy gradient theorem of Sutton and colleagues (2000), they naturally handle stochastic and continuous action spaces and underpin modern actor-critic and deep-RL algorithms.