Summary of Learning Abstract World Model For Value-preserving Planning with Options, by Rafael Rodriguez-sanchez and George Konidaris
Learning Abstract World Model for Value-preserving Planning with Options
by Rafael Rodriguez-Sanchez, George Konidaris
First submitted to arxiv on: 22 Jun 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 proposed method leverages a given set of temporally-extended actions to learn abstract Markov decision processes (MDPs) for general-purpose agents. This enables building state-action spaces at the correct abstraction level from sensorimotor experiences, increasing autonomy. The approach characterizes state abstractions necessary to ensure bounded value loss in the original MDP when planning with these skills. Evaluation in goal-based navigation environments shows that abstract model learning improves sample efficiency of planning and learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way for artificial agents to learn and make decisions. Right now, agents need lots of information and control over their actions to perform different tasks. This makes it hard for them to decide what to do next. The authors propose a solution by letting the agent build its own state-action space based on its experiences. This allows the agent to focus on more important things and make better decisions. The method is tested in simulations where agents need to navigate to goals, and it shows that this new approach helps agents learn faster and make better choices. |
