Summary of On Shallow Planning Under Partial Observability, by Randy Lefebvre et al.
On shallow planning under partial observability
by Randy Lefebvre, Audrey Durand
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 examine how different choices in designing reinforcement learning problems affect their performance. Specifically, they focus on the discount factor, which determines the planning horizon of an agent trying to maximize cumulative rewards. The team finds that a shorter planning horizon can be beneficial, especially when there is limited information about the environment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how selecting different discount factors in reinforcement learning affects the balance between good and bad predictions (bias-variance trade-off). They discover that using a shorter planning horizon can lead to better results, particularly when the agent doesn’t have complete information about its surroundings. |
Keywords
» Artificial intelligence » Reinforcement learning
