Web Reference: Goal: Find the parameter vector w that minimizes the loss between a true value function V (s) and its approximation ^V (s; w) as represented with a particular function class parameterized by w. Jul 23, 2025 · In reinforcement learning, the agent's goal is to learn a policy that maximizes cumulative reward over time. This involves estimating value functions, which predict future rewards, or directly approximating the policy, which maps states to actions. Nonlinear Value Function Approximation • Use artificial neural networks state S parameter
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