Summary of Ai Planning in the Imagination: High-level Planning on Learned Abstract Search Spaces, by Carlos Martin et al.
AI planning in the imagination: High-level planning on learned abstract search spaces
by Carlos Martin, Tuomas Sandholm
First submitted to arxiv on: 16 Aug 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 proposed PiZero method enables reinforcement learning agents to plan in an abstract search space learned during training, decoupled from the real environment. This allows for high-level planning at arbitrary timescales and reasoning about compound or temporally-extended actions. The method seamlessly handles continuous action spaces, combinatorial action spaces, and partial observability, outperforming comparable prior methods without assuming access to an environment simulator at execution time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence has made significant progress in many areas, but planning is still a key challenge. Imagine being able to plan ahead and make smart decisions before taking actions. This is what the PiZero method does – it lets agents learn to plan during training, so they can make better choices later on. The method is useful in situations where lots of small steps are needed to achieve a goal, like playing Pacman or solving complex puzzles. |
Keywords
» Artificial intelligence » Reinforcement learning
