Hierarchical Rl

[TOC] Title: Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning Author: Seungyong Moon et. al. Publish Year: 2 Nov 2023 Review Date: Tue, Apr 2, 2024 url: https://arxiv.org/abs/2307.03486 Summary of paper Contribution PPO agents demonstrate some ability to predict future achievements. Leveraging this observation, a novel contrastive learning method called achievement distillation is introduced, enhancing the agent’s predictive abilities. This approach excels at discovering hierarchical achievements, Some key terms Model based and explicit module in previous studies are not that good...

[TOC] Title: Hierarchical Decision Making by Generating and Following Natural Language Instructions Author: Hengyuan Hu et. al. FAIR Publish Year: 2019 Review Date: Dec 2021 Summary of paper One line summary: they build a Architect Builder model to clone human behaviour for playing RTS game Their task environment is very similar to IGLU competition setting, but their model is too task-specific The author mentioned some properties about natural language instructions...

Title: Modular Multitask Reinforcement Learning with Policy Sketches Author: Jacob Andreas et. al. Publish Year: 2017 Review Date: Dec 2021 Background info for this paper: Their paper describe a framework that is inspired by on options MDP, for which a reinforcement learning task is handled by several sub-MDP modules. (that is why they call it Modular RL) They consider a multitask RL problem in a shared environment. (See the figure below)....