Discussing the article: "Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)"
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Check out the new article: Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT).
In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.
Various offline reinforcement learning methods for solving this problem use parameterization or regularization, which constrain the Agent's policy to perform actions within the support set of the training dataset. Detailed constructions usually interfere with Agent models, which can lead to additional operational costs and prevent the full use of established online reinforcement learning methods. Regularization methods reduce the discrepancy between the learned policy and the training dataset, which may not meet the definition of density-based support and thus ineffectively avoid acting outside the distribution.
In this context, I suggest considering the applicability of the Supported Policy OpTimization (SPOT) method, which was presented in the article "Supported Policy Optimization for Offline Reinforcement Learning". Its approaches follow directly from a theoretical formalization of policy constraint based on the density distribution of the training dataset. SPOT uses a density estimator based on a Variational AutoEncoder (VAE), which is a simple yet effective regularization element. It can be built into ready-made reinforcement learning algorithms. SPOT achieves best-in-class performance on standard offline RL benchmarks. Thanks to its flexible design, models pre-trained offline using SPOT can also be fine-tuned online.
Author: Dmitriy Gizlyk