Summary of Transductive Reward Inference on Graph, by Bohao Qu et al.
Transductive Reward Inference on Graph
by Bohao Qu, Xiaofeng Cao, Qing Guo, Yi Chang, Ivor W. Tsang, Chengqi Zhang
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This study proposes a novel approach to reward information propagation graphs, enabling effective estimation of rewards for unlabelled data in offline reinforcement learning. Reward inference is crucial for developing practical policies, as direct environmental interactions are often costly or unethical and reward functions are rarely accessible. The research focuses on creating a reward inference method based on contextual graph properties, utilizing limited human annotations to infer rewards for unlabelled data. A reward propagation graph is constructed by incorporating influential factors into edge weights. Transductive reward inference estimates rewards for unlabelled data using the graph. The study demonstrates convergence to a local optimum and validates its effectiveness through empirical evaluations in locomotion and robotic manipulation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists developed a new way to figure out what’s important (rewards) without directly interacting with an environment. This is useful because it’s often too costly or unethical to interact directly, like in healthcare or robotics. The team used graph theory to create a method that can estimate rewards for things we don’t have labels for. They showed that this approach works by testing it on different tasks and improving the results. |
Keywords
* Artificial intelligence * Inference * Reinforcement learning
