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	Programmazione Dinamica Bayesiana	Apprendimento per rinforzo
Campo	Simulazione	Apprendimento profondo
Famiglia	Process / pipeline	Machine learning
Anno di origine	1957 (Bellman DP); Bayesian extensions 1990s–2000s	1950s–1998
Ideatore	Bellman, R.; extended by Bayesian frameworks (Duff, Bertsekas)	Sutton, R. S. & Barto, A. G. (formalised); Bellman, R. (foundations)
Tipo	Sequential optimization with Bayesian belief updating	Sequential decision-making framework
Fonte seminale	Bertsekas, D. P. (1995). Dynamic Programming and Optimal Control. Athena Scientific, Belmont, MA. ISBN: 9781886529267	Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. ISBN: 978-0-262-03924-6
Alias	BDP, Bayesian DP, Bayesian sequential optimization, Bayesian stochastic control	RL, reward-based learning, trial-and-error learning, policy optimization
Correlati	4	2
Sintesi	Bayesian Dynamic Programming (BDP) combines Bellman's dynamic programming framework with Bayesian inference to optimize sequential decisions when transition probabilities or reward structures are unknown. At each stage, the agent updates beliefs about the environment using observed outcomes, then computes an optimal policy that explicitly accounts for both immediate rewards and the value of information gained through exploration.	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.