Summary of On Policy Evaluation Algorithms in Distributional Reinforcement Learning, by Julian Gerstenberg et al.
On Policy Evaluation Algorithms in Distributional Reinforcement Learning
by Julian Gerstenberg, Ralph Neininger, Denis Spiegel
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Probability (math.PR)
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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 The novel class of algorithms introduced in this paper efficiently approximates the unknown return distributions in policy evaluation problems from distributional reinforcement learning. The proposed distributional dynamic programming algorithms are suitable for underlying Markov decision processes (MDPs) with an arbitrary probabilistic reward mechanism, including continuous reward distributions with unbounded support being potentially heavy-tailed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces new algorithms to solve a problem in machine learning called policy evaluation. These algorithms help figure out how good or bad a decision is by looking at the rewards it gets. The algorithms work for any kind of decision-making problem, even if the reward is not just 0 or 1, but can be anything. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
