Representation and Invariance in Reinforcement Learning
Quick summary
arXiv:2112.07752v5 Announce Type: replace Abstract: Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framework's environments, the agent must first be converted, or mapped, into that other framework. In this paper, we lay foundations for studying relative-intelligence-preserving mappability between RL frameworks. We introduce a criterion which is sufficient for relative intelligence to be preserved according to one particular method of measuring intelligence. We show that this criterion cannot be met when map
Key takeaways
- arXiv:2112.07752v5 Announce Type: replace Abstract: Researchers have formalized reinforcement learning (RL) in different ways.
- If an agent in one RL framework is to run within another RL framework's environments, the agent must first be converted, or mapped, into that other framework.
- In this paper, we lay foundations for studying relative-intelligence-preserving mappability between RL frameworks.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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