Summary of Foundations Of Multivariate Distributional Reinforcement Learning, by Harley Wiltzer et al.
Foundations of Multivariate Distributional Reinforcement Learning
by Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Mark Rowland
First submitted to arxiv on: 31 Aug 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 introduces algorithms for provably convergent multi-objective decision-making using reinforcement learning (RL) with multivariate reward signals. The authors develop oracle-free and computationally-tractable methods for multivariate distributional dynamic programming and temporal difference learning, which match familiar convergence rates in the scalar reward setting. Surprisingly, they show that standard analysis of categorical TD learning fails when the reward dimension is larger than 1, resolving this issue with a novel projection onto the space of mass-1 signed measures. The authors also identify tradeoffs between distribution representations that influence the performance of multivariate distributional RL in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can make good decisions when there are multiple things to consider. It introduces new ways for computers to learn from experience and make decisions based on rewards, like getting a high score or a reward point. The researchers found that if we have more than one thing to reward or punish a machine for, it’s harder to make sure the machine is learning correctly. They developed new methods to solve this problem and showed how they can be used in real-world applications. |
Keywords
» Artificial intelligence » Reinforcement learning
