Policy Gradient
A class of algorithms in reinforcement learning that directly optimize policy parameters, estimating gradients to maximize expected return. In large-model settings, policy gradient methods commonly face the problem of high variance.
Timeline (last 90 days)
Theoretical last-iterate convergence guarantees for Q-Learning have long lagged behind averaging-iterate analyses, as well as analogous results for methods such as policy gradient.
Policy gradient methods (such as PPO) rely on a discount factor γ to exponentially decay future rewards, with a reward t steps away from the current moment retaining only a weight of γ^t.
策略梯度定理表明期望回报对策略参数的梯度可表示为 ∇J(θ) = E[∇log π_θ(a|s) · A(s,a)]，其中A是优势函数
All Facts (3)
Theoretical last-iterate convergence guarantees for Q-Learning have long lagged behind averaging-iterate analyses, as well as analogous results for methods such as policy gradient.
50%UnverifiedPolicy gradient methods (such as PPO) rely on a discount factor γ to exponentially decay future rewards, with a reward t steps away from the current moment retaining only a weight of γ^t.
50%Unverified策略梯度定理表明期望回报对策略参数的梯度可表示为 ∇J(θ) = E[∇log π_θ(a|s) · A(s,a)]，其中A是优势函数
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