3 accepted papers
In recent years, offline reinforcement learning has attracted considerable attention in artificial intelligence. By generating a static dataset through a behavior policy, it is unable to engage in online interactions with the environment. However, this inevitably leads to states or actions undergoin…
Offline reinforcement learning is an approach for transforming static datasets into powerful decision engines. It cannot interact with the environment online, which leads to distribution shifts. Previous approaches addressed this problem by making the current policy as close as possible to the behav…
A powerful method for automated decision systems is Adversarial Imitation Learning (AIL). It is based on a generative adversarial framework that alternately optimizes a generator (learner) and a discriminator (reward function). In the popular mind, a high-accuracy discriminator results in informativ…