Summary of Bilinear Convolution Decomposition For Causal Rl Interpretability, by Narmeen Oozeer et al.
Bilinear Convolution Decomposition for Causal RL Interpretability
by Narmeen Oozeer, Sinem Erisken, Alice Rigg
First submitted to arxiv on: 1 Dec 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 research proposes a novel approach to interpret reinforcement learning (RL) models by replacing nonlinearities in convolutional neural networks (ConvNets) with bilinear variants. The study shows that these bilinear model variants perform comparably in model-free RL settings and provides a side-by-side comparison on ProcGen environments. The analytic structure of bilinear layers enables weight-based decomposition, allowing for the quantification of functional importance through eigendecomposition. The authors also propose a methodology for causally validating concept-based probes and demonstrate its utility by studying a maze-solving agent’s ability to track a cheese object. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning (RL) models are used to train artificial intelligence agents, but it’s hard to understand why they make certain decisions. To solve this problem, researchers replaced some parts of these models with simpler versions called bilinear layers. This new approach helps us figure out why the agent is making certain choices by breaking down the model into smaller, more understandable pieces. The study shows that using bilinear layers works just as well as traditional methods in many scenarios. |
Keywords
» Artificial intelligence » Reinforcement learning
