Summary of Value Of Information and Reward Specification in Active Inference and Pomdps, by Ran Wei
Value of Information and Reward Specification in Active Inference and POMDPs
by Ran Wei
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 A recently popularized quantity in active inference, expected free energy (EFE), is explored in this paper as a decision-making objective function. The study takes a bottom-up approach, examining how EFE-based agents compare to reward-driven reinforcement learning (RL) agents. By casting EFE under a specific class of belief MDPs and utilizing analysis tools from RL theory, the researchers show that EFE approximates the Bayes-optimal RL policy via information value. This work has implications for objective specification in active inference agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists investigate how well expected free energy (EFE) helps an agent make good decisions. EFE is a way to measure how well an agent’s predictions match reality. Researchers compare EFE-based agents with those that use rewards and learning to make choices. They show that EFE agents are close to being optimal in their decision-making, which has important implications for how these agents work. |
Keywords
» Artificial intelligence » Inference » Objective function » Reinforcement learning
