Summary of Learning Telic-controllable State Representations, by Nadav Amir et al.
Learning telic-controllable state representations
by Nadav Amir, Stas Tiomkin, Angela Langdon
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 presents a novel computational framework for state representation learning in bounded agents, where the descriptive and normative aspects are coupled through the notion of telic states. Telic controllability is introduced to characterize the tradeoff between the granularity of a telic state representation and the policy complexity required to reach all telic states. The authors propose an algorithm for learning controllable state representations, illustrated using a simple navigation task with shifting goals. This work highlights the crucial role of deliberate ignorance in balancing goal flexibility and policy complexity. By considering both descriptive and normative aspects, this framework advances a unified theoretical perspective on goal-directed state representation learning in natural and artificial agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how computers can learn to represent the world and make decisions based on goals. Normally, these two parts are separate: one for describing what’s happening and one for figuring out if it’s good or bad. But sometimes, they’re connected – like when you want to reach a specific goal. The authors came up with a new way to think about this called “telic states” and showed how it can help computers learn better. They used a simple example of finding your way around to illustrate their idea. |
Keywords
» Artificial intelligence » Representation learning
