New Automata Improve Discounted-Sum Payoff Categorization
Key takeaways
- Categorizer automata efficiently bin continuous data based on discounted sums of rewards.
- The new construction offers linear state space, improving on exponential complexity.
- It enables synthesizing optimal policies for Markov Decision Processes with complex utility functions.
- This method is exact for piecewise-constant utilities and epsilon-optimal for piecewise-Lipschitz utilities.
Who benefits
Summary
This paper introduces categorizer automata, a deterministic automaton that efficiently categorizes continuous data into discrete bins based on discounted sums of rewards. It offers a linear-state space construction, significantly improving upon exponential-state cross-products, and applies this to Markov decision processes for synthesizing policies that maximize expected utility, even with discontinuous utility functions.
Why it matters
This theoretical advancement offers more efficient and precise methods for decision-making in systems where continuous rewards need to be categorized and optimized over time, particularly in complex control or reinforcement learning scenarios.
How to implement this in your domain
- 1Explore the theoretical underpinnings for designing more efficient reward categorization systems in AI agents.
- 2Investigate how these automata could be applied to optimize policies in real-world Markov Decision Processes.
- 3Consider using this approach for systems requiring fine-grained control based on cumulative, discounted metrics.
- 4Evaluate the potential for improved performance in reinforcement learning tasks with complex reward structures.
Original post by Nathalie Bertrand, Pranav Ghorpade, Senthil Rajasekaran, Sasha Rubin, Moshe Vardi
"arXiv:2608.26763v1 Announce Type: new Abstract: Categorizing continuous data into discrete bins is a fundamental operation in artificial intelligence. We introduce the categorizer automaton, a deterministic automaton that reads an infinite sequence of rewards and identifies which…"
View on XOriginally posted by Nathalie Bertrand, Pranav Ghorpade, Senthil Rajasekaran, Sasha Rubin, Moshe Vardi on X · view source
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