Generative Models

[TOC] Title: Learning Generative Models With Goal Conditioned Reinforcement Learning Author: Mariana Vargas Vieyra et. al. Publish Year: 26 Mar 2023 Review Date: Thu, Mar 30, 2023 url: https://arxiv.org/abs/2303.14811 Summary of paper Contribution we present a novel framework for learning generative models with goal-conditioned reinforcement learning we define two agents, a goal conditioned agent (GC-agent) and a supervised agent (S-agent) Given a user-input initial state, the GC-agent learns to reconstruct the training set....

[TOC] Title: Deep RL With Hierarchical Action Exploration for Dialogue Generation Author: Itsugun Cho et. al. Publish Year: 22 Mar 2023 Review Date: Thu, Mar 30, 2023 url: https://arxiv.org/pdf/2303.13465v1.pdf Summary of paper Motivation Approximate dynamic programming applied to dialogue generation involves policy improvement with action sampling. However, such a practice is inefficient for reinforcement learning because the eligible (high action value) responses are very sparse, and the greedy policy sustained by the random sampling is flabby....