An operator view of policy gradient methods

We cast policy gradient methods as the repeated application of two operators: a policy improvement operator $\mathcal{I}$, which maps any policy $\pi$ to a better one $\mathcal{I}\pi$, and a projection operator $\mathcal{P}$, which finds the best approximation of $\mathcal{I}\pi$ in the set of realizable policies. We use this framework to introduce operator-based versions of traditional policy gradient methods such as REINFORCE and PPO, which leads to a better understanding of their original counterparts... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
PPO
Policy Gradient Methods
REINFORCE
Policy Gradient Methods