Summary of Continuously Evolving Rewards in An Open-ended Environment, by Richard M. Bailey
Continuously evolving rewards in an open-ended environment
by Richard M. Bailey
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 novel approach to dynamic reward updating in complex environments is proposed, using RULE: Reward Updating through Learning and Expectation. The algorithm is tested in a simplified ecosystem where agents must adapt to survive. Entities successfully abandon detrimental behaviors, amplify beneficial ones, and respond to novel items by endogenously modifying their underlying reward function during continuous learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers try to figure out how to make computer programs or “agents” learn from rewards in complex real-world environments. Right now, we can’t easily update the rules of what’s good or bad for these agents as the environment changes. The authors want to solve this problem by creating a new way to dynamically change the rewards an agent receives based on its actions and expectations. They test this idea in a simple simulated world where the agents have to survive and adapt. The results show that their method works well, allowing the agents to learn from experience and make good decisions. |
