Summary of Online Decision Metamorphformer: a Casual Transformer-based Reinforcement Learning Framework Of Universal Embodied Intelligence, by Luo Ji and Runji Lin
Online Decision MetaMorphFormer: A Casual Transformer-Based Reinforcement Learning Framework of Universal Embodied Intelligence
by Luo Ji, Runji Lin
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Online Decision MetaMorphFormer (ODM) framework aims to achieve self-awareness, environment recognition, and action planning through a unified model architecture. Building upon Reinforcement Learning (RL) with transformers, ODM addresses the limitation of offline training pipelines by enabling exploration and generalization abilities. This approach is motivated by cognitive and behavioral psychology, allowing agents to learn from others, recognize their environment, and practice based on their own experience. The framework can be applied to any arbitrary agent with a multi-joint body in different environments and trained with various tasks using large-scale pre-trained datasets. ODM quickly warms up and learns the necessary knowledge to perform desired tasks while the target environment reinforces the universal policy. Online experiments, few-shot, and zero-shot environmental tests verify ODM’s performance and generalization ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ODM is a new way for artificial intelligence (AI) to learn and adapt in different environments. It uses ideas from cognitive psychology to help AI agents become more self-aware and able to recognize their surroundings. This allows them to make better decisions and take actions that are appropriate for the situation. The ODM framework can be used with any type of AI agent, as long as it has a body with multiple joints. This means it could be applied to robots or other machines that need to move around in different environments. |
Keywords
» Artificial intelligence » Few shot » Generalization » Reinforcement learning » Zero shot
