Summary of Hierarchical Meta-reinforcement Learning Via Automated Macro-action Discovery, by Minjae Cho et al.
Hierarchical Meta-Reinforcement Learning via Automated Macro-Action Discovery
by Minjae Cho, Chuangchuang Sun
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Meta-Reinforcement Learning (Meta-RL) architecture is proposed to enable fast adaptation to new testing tasks across multiple complex and high-dimensional tasks. The three-level hierarchical structure consists of task representations, task-agnostic macro-actions, and primitive actions. This allows the policy to guide low-level primitive action learning towards goal states while addressing forgetting issues between tasks. The macro-action’s task-agnostic nature is achieved by removing task-specific components from the state space, enabling re-composition across different tasks. Training schemes are innovatively tailored to mitigate potential instability. Experimental results in the MetaWorld framework demonstrate improved sample efficiency and success rates compared to previous state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of learning called Meta-Reinforcement Learning helps computers quickly learn new skills when faced with a series of complex tasks. The approach involves breaking down big tasks into smaller, manageable parts, which makes it easier for the computer to adapt to new challenges. This is important because computers can sometimes “forget” what they learned earlier in favor of learning something new and conflicting. The proposed architecture addresses this issue by creating a way for the computer to learn general skills that can be applied to many different tasks. This leads to faster adaptation and better performance overall. |
Keywords
» Artificial intelligence » Reinforcement learning
