71、Policy Gradient Methods in Reinforcement Learning

Policy Gradient Methods in Reinforcement Learning

1. Introduction to Temporal Difference and Policy Gradient Concepts

In reinforcement learning, understanding the concepts of temporal difference and policy gradient methods is crucial.

1.1 Temporal Difference Learning

When $\lambda = 1$, the approach is equivalent to using Monte - Carlo evaluations to compute the ground - truth. At $\lambda = 1$, new error information is used to fully correct past mistakes without discount, resulting in an unbiased estimate. Here, $\lambda$ is used for step discounting, while $\gamma$ is used in computing the TD - error $\delta_t$. The parameter $\lambda$ is algorithm - specific, and $\gamma$ is environment - specific.

Using $\lambda = 1$ or Monte