Approximation Benefits of Policy Gradient Methods with Aggregated States

• URL: http://arxiv.org/abs/2007.11684v3
• Date: Thu, 23 Jun 2022 15:39:09 GMT
• Title: Approximation Benefits of Policy Gradient Methods with Aggregated States
• Authors: Daniel Russo
• Abstract summary: Folklore suggests that policy gradient can be more robust to misspecification than its relative, approximate policy iteration. This paper shows a policy gradient method converges to a policy whose regret per-period is bounded by $epsilon$.
• Score: 8.348171150908724
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Folklore suggests that policy gradient can be more robust to misspecification than its relative, approximate policy iteration. This paper studies the case of state-aggregated representations, where the state space is partitioned and either the policy or value function approximation is held constant over partitions. This paper shows a policy gradient method converges to a policy whose regret per-period is bounded by $\epsilon$, the largest difference between two elements of the state-action value function belonging to a common partition. With the same representation, both approximate policy iteration and approximate value iteration can produce policies whose per-period regret scales as $\epsilon/(1-\gamma)$, where $\gamma$ is a discount factor. Faced with inherent approximation error, methods that locally optimize the true decision-objective can be far more robust.

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