Why does policy gradiet method has high variance?

时间 2021-01-04

标签高方差策略梯度繁體版

原文原文链接

策略梯度方法策略梯度方法中，目标函数是使得整个episode得到的reward的均值最大： maximizeθEπθ[∑t=0T−1γtrt] 由于： ∇θE[f(x)]=∇θ∫pθ(x)f(x)dx=∫pθ(x)pθ(x)∇θpθ(x)f(x)dx=∫pθ(x)∇θlogpθ(x)f(x)dx=E[f(x)∇θlogpθ(x)] 以及： ∇θlogpθ(τ)=∇log(μ(s0)∏t=0T−1

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1. Why does deep learning work?
2. why request method is OPTIONS
3. Why does Double.NaN==Double.NaN return false?
4. A Policy Update Strategy in Model-free Policy Search: Policy Gradient
5. ModelMapper报错Ensure that method has zero parameters and does not return void.
6. Policy Gradient Algorithms
7. （转）RL — Policy Gradient Explained
8. Why UI correction note always has a big static size
9. Where does the error come from?----Bias and Variance
10. Privacy Policy

>>更多相关文章<<