Summary of Partial Models For Building Adaptive Model-based Reinforcement Learning Agents, by Safa Alver et al.
Partial Models for Building Adaptive Model-Based Reinforcement Learning Agents
by Safa Alver, Ali Rahimi-Kalahroudi, Doina Precup
First submitted to arxiv on: 27 May 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 This paper addresses a crucial issue in reinforcement learning, where modern model-based agents exhibit poor adaptivity to local changes in the environment. Current agents are designed to optimize sample efficiency in single-task settings, neglecting the challenges that arise in other settings. The primary challenge in local adaptation is building and maintaining an accurate model after a change. Deep model-based agents struggle due to their monolithic structures lacking distribution shift handling capabilities. This paper proposes the conceptually simple idea of partial models to overcome this challenge. By modeling different state space parts through distinct models, agents can maintain accuracy across the state space while quickly adapting to local changes. The authors demonstrate the effectiveness of partial models in deep model-based agents like Dyna-Q, PlaNet, and Dreamer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how some smart computer systems, called model-based agents, struggle to adapt when their environment changes. Right now, these agents are really good at getting better at one task, but they’re not very good at changing to a new situation. The main problem is that they have trouble building an accurate picture of what’s happening in the world after it changes. This paper proposes a simple idea called “partial models” that could help these agents adapt more easily. By breaking down the agent’s understanding of the world into smaller pieces, it can quickly adjust when things change. |
Keywords
» Artificial intelligence » Reinforcement learning
