Summary of Traversing Pareto Optimal Policies: Provably Efficient Multi-objective Reinforcement Learning, by Shuang Qiu et al.
Traversing Pareto Optimal Policies: Provably Efficient Multi-Objective Reinforcement Learning
by Shuang Qiu, Dake Zhang, Rui Yang, Boxiang Lyu, Tong Zhang
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 paper investigates multi-objective reinforcement learning (MORL), which aims to learn Pareto optimal policies in the presence of multiple reward functions. Despite MORL’s empirical success, there is a lack of understanding regarding various optimization targets and efficient learning algorithms. The authors systematically analyze several optimization targets to assess their ability to find all Pareto optimal policies and controllability over learned policies by preferences for different objectives. Tchebycheff scalarization is identified as a favorable scalarization method, and the non-smoothness of this approach is reformulated into a new min-max-max optimization problem. Efficient algorithms are proposed for the stochastic policy class to learn Pareto optimal policies. An online UCB-based algorithm achieves an ε learning error with an Õ(ε^(-2)) sample complexity for a single given preference, while a preference-free framework reduces environment exploration costs under different preferences. The authors prove that this framework only requires an Õ(ε^(-2)) exploration complexity in the exploration phase and demands no additional exploration afterward. Finally, the smooth Tchebycheff scalarization is analyzed, which proves to be more advantageous in distinguishing Pareto optimal policies from other weakly Pareto optimal policies based on entry values of preference vectors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way for computers to learn and make decisions when there are multiple goals. It’s called multi-objective reinforcement learning (MORL). Right now, MORL is successful but we don’t really understand how it works or what makes it work well. The authors of this paper looked at different ways to optimize MORL and found one that works really well – Tchebycheff scalarization. They also came up with new algorithms for computers to use when making decisions based on multiple goals. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
