Summary of Exploiting Exogenous Structure For Sample-efficient Reinforcement Learning, by Jia Wan et al.
Exploiting Exogenous Structure for Sample-Efficient Reinforcement Learning
by Jia Wan, Sean R. Sinclair, Devavrat Shah, Martin J. Wainwright
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); 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 This research paper investigates a novel class of Markov Decision Processes (MDPs) called Exo-MDPs, which have applications in domains such as inventory control, portfolio management, and ride-sharing. The key feature of Exo-MDPs is the separation of state space into exogenous and endogenous components, allowing for stochastic evolution of exogenous states independent of agent actions. The paper establishes a representational equivalence between discrete MDPs, Exo-MDPs, and discrete linear mixture MDPs, demonstrating that any discrete MDP can be represented as an Exo-MDP with linear transition and reward dynamics based on the exogenous state distribution. Additionally, the authors prove upper bounds on regret for unobserved exogenous states and validate their findings through experiments on inventory control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Exo-MDPs are a special type of Markov Decision Process that helps us make decisions in situations where some things happen randomly and others depend on our actions. This is useful in real-life scenarios like managing inventory, investing money, or getting people to places they want to go. The research shows that Exo-MDPs can be broken down into simpler parts, making it easier to understand how they work and how well they do. The results also help us make better decisions by showing how much information we need to know in order to make good choices. |
