Summary of Hindsight Priors For Reward Learning From Human Preferences, by Mudit Verma et al.
Hindsight PRIORs for Reward Learning from Human Preferences
by Mudit Verma, Katherine Metcalf
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 paper presents a novel approach to Preference-based Reinforcement Learning (PbRL) that addresses the credit assignment problem, which hinders the learning of reward functions from preference feedback. The proposed method, Hindsight PRIOR, uses a world model to approximate state importance within a trajectory and guides rewards to be proportional to state importance through an auxiliary predicted return redistribution objective. This approach improves the speed of policy learning, overall policy performance, and reward recovery on locomotion and manipulation tasks. Experimental results demonstrate significant improvements in reward recovery on MetaWorld (20%) and DMC (15%). The paper shows that even a simple credit assignment strategy can have a positive impact on reward learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in artificial intelligence called Preference-based Reinforcement Learning. It’s like when you play games with friends and decide who wins or loses, but now computers can do this too! The old way of doing this was slow and didn’t work well, so the researchers came up with a new idea to speed it up. They used a “world model” to figure out which parts of an action were most important for making a good decision. This helped the computer learn faster and make better choices. The results show that this new method works really well on different types of games, like playing video games or using robots. |
Keywords
* Artificial intelligence * Reinforcement learning
