Summary of Learning Causally Invariant Reward Functions From Diverse Demonstrations, by Ivan Ovinnikov et al.
Learning Causally Invariant Reward Functions from Diverse Demonstrations
by Ivan Ovinnikov, Eugene Bykovets, Joachim M. Buhmann
First submitted to arxiv on: 12 Sep 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 paper proposes a novel regularization approach for inverse reinforcement learning methods to improve reward function generalization. The common challenge with inverse RL is that expert demonstrations can contain spurious correlations, leading to behavioral overfitting when training a policy on the learned reward function under distribution shift of environment dynamics. To address this issue, the authors develop a causal invariance principle-based regularization method for both exact and approximate formulations of the learning task. They demonstrate superior policy performance using the recovered reward functions in a transfer setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to figure out what rewards someone is seeking when we only see their actions. It’s like trying to guess why someone chose a certain path on a hike just by looking at their footprints. The problem is that people might follow different paths for different reasons, so it’s hard to get the right answer. To solve this problem, the authors came up with a new way to make sure we don’t pick up false clues and end up choosing the wrong reward. They tested their method on some examples and showed that it works better than other approaches. |
Keywords
» Artificial intelligence » Generalization » Overfitting » Regularization » Reinforcement learning
