Mirror Descent Actor Critic via Bounded Advantage Learning

• URL: http://arxiv.org/abs/2502.03854v1
• Date: Thu, 06 Feb 2025 08:14:03 GMT
• Title: Mirror Descent Actor Critic via Bounded Advantage Learning
• Authors: Ryo Iwaki,
• Abstract summary: Mirror Descent Value Iteration (MDVI) uses both Kullback-Leibler divergence and entropy as regularizers in its value and policy updates. We propose Mirror Descent Actor Critic (MDAC) as an actor-critic style instantiation of MDVI for continuous action domains.
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• Abstract: Regularization is a core component of recent Reinforcement Learning (RL) algorithms. Mirror Descent Value Iteration (MDVI) uses both Kullback-Leibler divergence and entropy as regularizers in its value and policy updates. Despite its empirical success in discrete action domains and strong theoretical guarantees, the performance of a MDVI-based method does not surpass an entropy-only-regularized method in continuous action domains. In this study, we propose Mirror Descent Actor Critic (MDAC) as an actor-critic style instantiation of MDVI for continuous action domains, and show that its empirical performance is significantly boosted by bounding the actor's log-density terms in the critic's loss function, compared to a non-bounded naive instantiation. Further, we relate MDAC to Advantage Learning by recalling that the actor's log-probability is equal to the regularized advantage function in tabular cases, and theoretically discuss when and why bounding the advantage terms is validated and beneficial. We also empirically explore a good choice for the bounding function, and show that MDAC perfoms better than strong non-regularized and entropy-only-regularized methods with an appropriate choice of the bounding function.

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