Summary of Reward Design For Justifiable Sequential Decision-making, by Aleksa Sukovic et al.
Reward Design for Justifiable Sequential Decision-Making
by Aleksa Sukovic, Goran Radanovic
First submitted to arxiv on: 24 Feb 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 This paper proposes a novel reward model for reinforcement learning agents, enabling them to justify their decisions using supporting evidence. The approach involves a zero-sum debate game where two agents provide evidence for competing decisions, which is evaluated by a proxy human judge. The reward model is designed to quantify the justifiability of a decision in a particular state, promoting accountable decision-making. The authors demonstrate the effectiveness of this approach in learning policies for prescribing and justifying treatment decisions for septic patients, outperforming policies trained solely on environment rewards while maintaining comparable performance. Furthermore, the debate-based feedback is shown to be comparable to ideal judge proxies, suggesting that the debate game outputs key information relevant for evaluating decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines make better decisions by having them explain why they made those decisions. This is important because we want our machines to be accountable and make sense to humans. The researchers created a game where two agents take turns providing evidence for different choices, and then a judge decides which choice is more justified. They tested this approach on training machines to prescribe treatments for patients with septic shock, and the results were promising. This new way of teaching machines can lead to better decision-making that humans understand. |
Keywords
* Artificial intelligence * Reinforcement learning
