RLingua: Leveraging Large Language Models to Improve Reinforcement Learning Sample Efficiency in Robotic Manipulations

Centrala begrepp

The author proposes RLingua, a framework that utilizes large language models to reduce the sample complexity of reinforcement learning in robotic manipulations. By generating rule-based controllers from LLMs and refining them through RL, RLingua significantly enhances the efficiency of RL processes.

Sammanfattning

The paper introduces RLingua, a novel framework that leverages large language models (LLMs) to improve sample efficiency in reinforcement learning for robotic manipulations. By extracting prior knowledge from LLMs to generate robot controllers and using them to collect training data, RLingua reduces the number of samples needed for effective RL. The study demonstrates the effectiveness of RLingua in reducing sample complexity and achieving high success rates in challenging tasks with sparse rewards. Real-world experiments validate the transferability of learned policies to actual robot tasks.

Key Points:

• Proposal of RLingua framework leveraging LLMs for improved sample efficiency in reinforcement learning.
• Extraction of prior knowledge from LLMs to generate rule-based robot controllers.
• Utilization of LLM-generated controllers for data collection and policy training.
• Reduction in sample complexity and high success rates demonstrated in various robotic manipulation tasks.
• Validation through real-world experiments showcasing transferability of learned policies.

Statistik

"RLingua can significantly reduce the sample complexity of TD3 in the robot tasks of panda_gym."
"RLingua achieves high success rates in sparsely rewarded robot tasks in RLBench."
"RLingua effectively addresses path planning issues through reinforcement learning."

Citat

"The rapid development of large language models (LLMs) has enabled us to obtain human-level responses across a broad range of professional fields."
"RLingua harnesses the extensive prior knowledge embedded in LLMs about robot motions and coding to significantly enhance RL processes."
"RLingua not only matches perfect success rates but also reaches high success rates with fewer training steps compared to standard algorithms."

Viktiga insikter från

RLingua

by Liangliang C... på arxiv.org 03-12-2024

https://arxiv.org/pdf/2403.06420.pdf

Djupare frågor

How can RLingua's approach be extended beyond robotic manipulations

RLingua's approach can be extended beyond robotic manipulations to various other domains where reinforcement learning is applied. For example, in autonomous vehicles, RLingua could leverage large language models to generate controllers for tasks such as lane following, obstacle avoidance, and decision-making at intersections. In healthcare, RLingua could assist in optimizing treatment plans or resource allocation in hospitals. Additionally, in finance, RLingua could help develop trading strategies or risk management policies based on the internal knowledge of LLMs. The key lies in adapting the prompt design and training process to suit the specific requirements of each domain while leveraging the prior knowledge embedded in LLMs.

What are potential drawbacks or limitations of relying on large language models for generating reward functions

One potential drawback of relying on large language models (LLMs) for generating reward functions is their interpretability and generalizability limitations. LLMs may struggle with capturing nuanced task dynamics or understanding complex environments accurately. This can lead to suboptimal performance or even unsafe behaviors when using LLM-generated reward functions for reinforcement learning algorithms. Moreover, there might be challenges related to bias and fairness when utilizing LLMs due to their training data sources and inherent biases encoded within them. Additionally, the computational resources required for training and fine-tuning LLMs can be substantial, making it costly and time-consuming to rely solely on them for generating reward functions.

How might advancements in multi-modal LLMs impact the future development of frameworks like RLingua

Advancements in multi-modal large language models (LLMs) are likely to have a significant impact on the future development of frameworks like RLingua. These advancements enable LLMs to process multiple types of data inputs simultaneously (e.g., text, images, audio), allowing for more comprehensive understanding and generation capabilities. In the context of RLingua, multi-modal LLMs could enhance the prompt design process by incorporating diverse forms of information into controller generation tasks. This would enable more robust rule-based controllers that consider a broader range of factors influencing task performance.
Furthermore, multi-modal LLMs may improve transfer learning capabilities within frameworks like RLingua by facilitating better adaptation across different domains with varying input modalities.
Overall,
the integration
of multi-modal
capabilities
into
frameworks like
RLinguacouldenhanceperformanceandflexibilityinroboticmanipulationsandotherreinforcementlearningapplicationsbyenablingmorecomprehensiveunderstandingandreliablecontrollergenerationbasedonvarieddatainputs.