Summary of Words As Beacons: Guiding Rl Agents with High-level Language Prompts, by Unai Ruiz-gonzalez et al.
Words as Beacons: Guiding RL Agents with High-Level Language Prompts
by Unai Ruiz-Gonzalez, Alain Andres, Pedro G.Bascoy, Javier Del Ser
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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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 teacher-student reinforcement learning framework leverages Large Language Models (LLMs) as “teachers” to guide the agent’s learning process by decomposing complex tasks into subgoals. The LLMs understand RL environments based on textual descriptions and provide subgoals for accomplishing tasks, similar to how humans would do. Three types of subgoals are proposed: positional targets, object representations, and language-based instructions generated by the LLM. Experimental results show that this curriculum-based approach accelerates learning and enhances exploration in complex tasks, achieving up to 30-200 times faster convergence compared to recent baselines designed for sparse reward environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way for artificial agents to learn in complex situations where rewards are hard to come by. It uses special kinds of computers called Large Language Models as “teachers” to help the agents figure out what to do next. The teachers break down big tasks into smaller, more manageable steps, and they even provide instructions written in human language! Researchers tested this approach on several environments and found that it helps the agents learn much faster than usual. This could be a game-changer for how we train AI systems. |
Keywords
* Artificial intelligence * Reinforcement learning
