Summary of A Survey on Enhancing Reinforcement Learning in Complex Environments: Insights From Human and Llm Feedback, by Alireza Rashidi Laleh et al.
A Survey On Enhancing Reinforcement Learning in Complex Environments: Insights from Human and LLM Feedback
by Alireza Rashidi Laleh, Majid Nili Ahmadabadi
First submitted to arxiv on: 20 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 A reinforcement learning (RL) survey paper explores the challenges and limitations of current RL methodologies in tackling real-world problems. Specifically, the curse of dimensionality hinders RL agents from achieving optimal performance when navigating complex environments with large observation spaces, leading to sample-inefficiency and prolonged learning times. The authors investigate how augmenting RL agents with human or large language models’ (LLMs) feedback can enhance performance and accelerate learning. This collaboration enables RL agents to discern relevant environmental cues and optimize decision-making processes. The paper focuses on two-fold problems: firstly, it examines the ways in which humans or LLMs assist RL agents; secondly, it delves into research papers addressing the intricacies of environments with large observation spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a type of machine learning that helps computers make decisions. Right now, this technology isn’t perfect because it gets stuck when it has to deal with lots of information. This makes it hard for the computer to learn quickly and make good choices. To solve this problem, scientists think about how humans or special language models can help the computer make better decisions. This is like having a friend who knows what’s important and can guide you through a tricky situation. The paper looks at two main challenges: one is how humans or language models can help the computer learn faster and better; the other is how scientists have already tried to solve this problem in different ways. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
