Summary of External Model Motivated Agents: Reinforcement Learning For Enhanced Environment Sampling, by Rishav Bhagat et al.
External Model Motivated Agents: Reinforcement Learning for Enhanced Environment Sampling
by Rishav Bhagat, Jonathan Balloch, Zhiyu Lin, Julia Kim, Mark Riedl
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A machine learning framework is proposed to improve the adaptability of reinforcement learning (RL) agents in dynamic environments without modifying their rewards. The framework, comprising interest fields and behavior shaping via interest fields, enables RL agents to balance task focus with learning about changing environments. The proposed method outperforms baselines on metrics measuring both efficiency and performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way is discovered to help machines learn better when the environment they’re in changes often. This is inspired by how humans can do many things at once and still adapt to new situations. The idea is to make machine learning models more interested in what’s happening around them, so they can figure out how to adjust quickly. Two special parts are used to make this happen: “interest fields” that help the model notice important changes, and “behavior shaping” that helps it decide what actions to take next. This new way works better than previous methods when tested. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
