Summary of Zero-shot Reinforcement Learning Via Function Encoders, by Tyler Ingebrand et al.
Zero-Shot Reinforcement Learning via Function Encoders
by Tyler Ingebrand, Amy Zhang, Ufuk Topcu
First submitted to arxiv on: 30 Jan 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 In this paper, researchers propose a novel approach called the “function encoder” to enable zero-shot transfer across related tasks in reinforcement learning (RL). The challenge lies in finding a good representation for the current task that connects it to previously seen tasks. By representing the reward function or transition function as a weighted combination of learned basis functions, the agent can achieve coherence and transfer between tasks without additional training. This approach is demonstrated to improve data efficiency, asymptotic performance, and training stability in three RL fields. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning (RL) helps machines make smart decisions. But sometimes, it’s hard for agents to learn new things if they’ve never seen something similar before. To fix this, scientists invented a “function encoder” that helps agents understand how new tasks relate to old ones. This means the agent can pick up new skills without needing more practice! The paper shows that using the function encoder makes RL better and faster in three important areas. |
Keywords
* Artificial intelligence * Encoder * Reinforcement learning * Zero shot
