Summary of Mdp Geometry, Normalization and Reward Balancing Solvers, by Arsenii Mustafin et al.
MDP Geometry, Normalization and Reward Balancing Solvers
by Arsenii Mustafin, Aleksei Pakharev, Alex Olshevsky, Ioannis Ch. Paschalidis
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 In this paper, researchers introduce a new way to understand Markov Decision Processes (MDPs) by normalizing the value function at each state without changing the advantage of any action with respect to any policy. This novel approach motivates a class of algorithms called Reward Balancing that solve MDPs by iterating through these transformations until an approximately optimal policy is found. The authors provide a convergence analysis of several algorithms in this class, including improvements upon current sample complexity results for MDPs with unknown transition probabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MDPs are a type of decision-making problem where you need to make choices based on uncertain outcomes. Imagine you’re playing a game where you can take different actions and the outcome depends on what happens next. This paper helps us understand how to make better decisions in these types of situations by introducing a new way of looking at the problem called Reward Balancing. It’s like finding a shortcut to get to the best possible solution. |
