Summary of Assessing the Zero-shot Capabilities Of Llms For Action Evaluation in Rl, by Eduardo Pignatelli et al.
Assessing the Zero-Shot Capabilities of LLMs for Action Evaluation in RL
by Eduardo Pignatelli, Johan Ferret, Tim Rockäschel, Edward Grefenstette, Davide Paglieri, Samuel Coward, Laura Toni
First submitted to arxiv on: 19 Sep 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 tackles the temporal credit assignment problem in Reinforcement Learning (RL), where delayed and sparse feedback makes it challenging to evaluate actions. Existing solutions require extensive domain knowledge and manual intervention, limiting their scalability. To overcome this, the authors introduce Credit Assignment with Language Models (CALM), a novel approach that leverages Large Language Models (LLMs) for reward shaping and options discovery. CALM decomposes tasks into elementary subgoals, assesses their achievement in state-action transitions, and provides an auxiliary reward signal when an option terminates. Preliminary evaluation on MiniHack dataset suggests that LLMs can effectively assign credit without examples or fine-tuning, indicating a promising prior for credit assignment in RL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines learn better from mistakes. When machines don’t get feedback right away, it’s hard to figure out what went wrong and how to improve. The authors want to solve this problem by using special language models that can help machines learn faster. They call their new method “Credit Assignment with Language Models” or CALM for short. CALM breaks down big tasks into smaller steps and gives the machine a reward when it does something right. This helps the machine learn better without needing as much feedback from humans. The authors tested this idea on some robot games and found that it works pretty well, even when the machines didn’t have any examples or special training. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning
