Summary of Instruction Following with Goal-conditioned Reinforcement Learning in Virtual Environments, by Zoya Volovikova et al.
Instruction Following with Goal-Conditioned Reinforcement Learning in Virtual Environments
by Zoya Volovikova, Alexey Skrynnik, Petr Kuderov, Aleksandr I. Panov
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The paper proposes a hierarchical framework for executing complex language instructions within virtual environments. It combines large language models (LLMs) for deep language comprehension with reinforcement learning agents for adaptive action-execution. The LLM translates language instructions into high-level action plans, which are then executed by the pre-trained reinforcement learning agent. The approach is demonstrated in two environments: IGLU and Crafter. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps artificial intelligence agents understand complex language instructions and perform tasks in virtual environments. It combines two types of AI models to make it work. One model understands language well, while the other model takes actions based on what it’s told. This combination lets agents follow instructions and complete tasks like building structures or interacting with objects. |
Keywords
* Artificial intelligence * Reinforcement learning
