Summary of Reinforcement Learning From Delayed Observations Via World Models, by Armin Karamzade et al.
Reinforcement Learning from Delayed Observations via World Models
by Armin Karamzade, Kyungmin Kim, Montek Kalsi, Roy Fox
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper proposes a novel approach to address observation delays in partially observable environments, leveraging world models to handle partial observability. The authors reduce delayed POMDPs to delayed MDPs using world models, enabling effective handling of partial observability where existing approaches degrade quickly. The proposed methods are evaluated on visual delayed environments, showcasing delay-aware reinforcement learning continuous control with visual observations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps robots learn better by taking into account the time it takes for them to get feedback about their actions. Usually, agents assume they’ll get immediate feedback, but in real life, this isn’t always true due to physical constraints. The authors come up with a way to use world models to handle delayed observations and partial observability. This leads to better performance than previous approaches. |
Keywords
* Artificial intelligence * Reinforcement learning
