Summary of Generalizing Multi-step Inverse Models For Representation Learning to Finite-memory Pomdps, by Lili Wu et al.
Generalizing Multi-Step Inverse Models for Representation Learning to Finite-Memory POMDPs
by Lili Wu, Ben Evans, Riashat Islam, Raihan Seraj, Yonathan Efroni, Alex Lamb
First submitted to arxiv on: 22 Apr 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 The abstract presents a challenge in scaling reinforcement learning (RL) algorithms by discovering an agent-centric state representation. This representation must encode relevant information while discarding irrelevant data. The authors focus on high-dimensional non-Markovian settings, where the current observation is insufficient to decode the informative state. They propose adapting generalized inverse models for learning agent-centric state representations and provide asymptotic theory in deterministic dynamics settings as well as counter-examples for alternative intuitive algorithms. An empirical study demonstrates the abilities of these alternatives in discovering agent-centric states. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Discovering an agent-centric state representation is crucial for scaling reinforcement learning (RL) algorithms. The authors tackle this challenge by focusing on high-dimensional non-Markovian settings, where observations are insufficient to decode the informative state. They propose a new approach using generalized inverse models and provide examples of how it can be applied. |
Keywords
» Artificial intelligence » Reinforcement learning
