Hamilton-Jacobi-Bellman Equation: Reinforcement Learning and Diffusion Models
Richard Bellman's 1952 paper established the foundation for optimal control and reinforcement learning. His later work in the 1950s connected continuous-time systems to a previously published physical result from the 1840s, formulating the optimal condition as a partial differential equation (PDE).
dani2442.github.io
16 min
13h ago
Hamilton-Jacobi-Bellman Equation: Reinforcement Learning and Diffusion Models
Richard Bellman's 1952 paper established the foundation for optimal control and reinforcement learning. His later work in the 1950s connected continuous-time systems to a previously published physical result from the 1840s, formulating the optimal condition as a partial differential equation (PDE).
dani2442.github.io
16 min
13h ago
Hamilton-Jacobi-Bellman Equation: Reinforcement Learning and Diffusion Models
Richard Bellman's 1952 paper established the foundation for optimal control and reinforcement learning. His later work in the 1950s connected continuous-time systems to a previously published physical result from the 1840s, formulating the optimal condition as a partial differential equation (PDE).
dani2442.github.io
16 min
13h ago
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