Summary of From Laws to Motivation: Guiding Exploration Through Law-based Reasoning and Rewards, by Ziyu Chen and Zhiqing Xiao and Xinbei Jiang and Junbo Zhao
From Laws to Motivation: Guiding Exploration through Law-Based Reasoning and Rewards
by Ziyu Chen, Zhiqing Xiao, Xinbei Jiang, Junbo Zhao
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Machine learning researchers have developed two powerful approaches for building autonomous agents: Large Language Models (LLMs) and Reinforcement Learning (RL). However, current agents often struggle to develop long-term strategies or make decisions due to limited understanding of the game environment. To address this challenge, a new method extracts experience from interaction records to model the underlying laws of the game environment, using these experiences as internal motivation to guide agents. This approach expresses experiences in language and can either assist agents in reasoning directly or be transformed into rewards for guiding training. The evaluation results demonstrate that both RL and LLM agents benefit from this experience, leading to improved overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous agents are getting smarter, but they’re not perfect yet! Researchers have found that these agents often get stuck because they don’t fully understand the game or environment they’re playing in. To fix this problem, scientists came up with a new idea: extract experiences from what happened during interactions and use them to help guide the agent’s decisions. This experience is like a set of rules that the agent can follow to make better choices. The results show that both types of agents (RL and LLM) get better when they use this experience, which means we might be able to create even more advanced autonomous systems in the future! |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
