Discussing the article: "Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL)"
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Check out the new article: Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL).
In this article, we will get acquainted with an interesting algorithm that is built at the intersection of supervised and reinforcement learning methods.
Behavior cloning methods, largely based on the principles of supervised learning, show fairly good results. But their main problem remains the search for ideal role models, which are sometimes very difficult to collect. In turn, reinforcement learning methods are able to work with non-optimal raw data. At the same time, they can find suboptimal policies to achieve the goal. However, when searching for an optimal policy, we often encounter an optimization problem that is more relevant in high-dimensional and stochastic environments.
To bridge the gap between these two approaches, a group of scientists proposed the Distance Weighted Supervised Learning (DWSL) method and presented it in the article "Distance Weighted Supervised Learning for Offline Interaction Data". It is an offline supervised learning algorithm for goal-conditioned policy. Theoretically, DWSL converges to an optimal policy with a minimum return boundary at the level of trajectories from the training set. The practical examples in the article demonstrate the superiority of the proposed method over imitation learning and reinforcement learning algorithms. I suggest taking a closer look at this DWSL algorithm. We will evaluate its strengths and weaknesses in solving our practical problems.
Author: Dmitriy Gizlyk