Policy Gradient Methods


REINFORCE is a Monte Carlo variant of a policy gradient algorithm in reinforcement learning. The agent collects samples of an episode using its current policy, and uses it to update the policy parameter $\theta$. Since one full trajectory must be completed to construct a sample space, it is updated as an off-policy algorithm.

$$ \nabla_{\theta}J\left(\theta\right) = \mathbb{E}_{\pi}\left[G_{t}\nabla_{\theta}\ln\pi_{\theta}\left(A_{t}\mid{S_{t}}\right)\right]$$

Image Credit: Tingwu Wang


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