ідея - Reinforcement Learning - # Offline Reinforcement Learning with Large Language Model Rollouts

Leveraging Large Language Models to Enhance Reinforcement Learning Agents' Skill Acquisition

Q: How can KALM be extended to handle more complex environments, such as those with visual observations or continuous action spaces?

To extend KALM to handle more complex environments, such as those with visual observations or continuous action spaces, several modifications and enhancements can be implemented: Incorporating Vision Encoders: For environments with visual observations, integrating vision encoders into the LLM architecture can enable the model to process visual data. By incorporating convolutional neural networks (CNNs) or other vision models, the LLM can interpret images and extract relevant features for generating rollouts. Multi-Modal Inputs: Adapting the LLM to accept multi-modal inputs, including text, images, and numerical data, can enhance its understanding of diverse environmental information. By incorporating different input modalities, the LLM can generate more comprehensive and contextually rich rollouts. Continuous Action Spaces: To handle environments with continuous action spaces, the LLM architecture can be modified to output continuous action values rather than discrete actions. This adjustment allows the model to generate precise and continuous action sequences, enabling agents to navigate environments with fine-grained control. Hybrid Architectures: Implementing hybrid architectures that combine LLMs with specialized models designed for specific tasks, such as reinforcement learning with continuous action spaces, can further enhance the model's performance in complex environments. By leveraging the strengths of different models, KALM can adapt to a wider range of environments and tasks.

Q: How might the integration of KALM with other offline RL algorithms or multi-modal models impact the overall performance and generalization capabilities of the learned policies?

Integrating KALM with other offline RL algorithms or multi-modal models can have several benefits for the overall performance and generalization capabilities of the learned policies: Enhanced Skill Acquisition: Combining KALM with diverse offline RL algorithms can leverage the strengths of each approach, leading to more robust skill acquisition. By integrating multiple algorithms, the agent can learn from a broader range of data sources and improve its performance across various tasks. Improved Generalization: The integration of multi-modal models with KALM can enhance the model's ability to generalize to different types of data inputs. By incorporating visual, textual, and numerical information, the model can develop a more comprehensive understanding of the environment, leading to improved generalization to novel tasks and scenarios. Increased Adaptability: By combining KALM with other offline RL algorithms or multi-modal models, the learned policies can become more adaptable to diverse environments and tasks. The integration of different techniques can provide complementary benefits, enhancing the agent's adaptability and performance in complex settings. Efficient Training: Integrating KALM with efficient offline RL algorithms can streamline the training process and improve the convergence speed of the learned policies. By leveraging the strengths of different algorithms, the training process can be optimized for faster and more effective policy learning. Overall, the integration of KALM with other offline RL algorithms or multi-modal models can lead to synergistic effects, enhancing the performance, generalization capabilities, and adaptability of the learned policies in diverse and complex environments.

Основні поняття

Integrating large language models with offline reinforcement learning can enable agents to acquire novel skills by generating diverse and meaningful imaginary rollouts that reflect unprecedented optimal behaviors.

Анотація

The paper introduces a novel method called Knowledgeable Agents from Language Model Rollouts (KALM) that leverages the knowledge embedded in large language models (LLMs) to enhance the skill acquisition of reinforcement learning (RL) agents. The key idea is to utilize the LLM to generate imaginary rollouts that capture a broader range of skills, including those not present in the original offline dataset, and then integrate these rollouts with offline RL to train more versatile and informed agents.

The main components of KALM are:

LLM Grounding: KALM fine-tunes the LLM to perform various tasks based on environmental data, including bidirectional translation between natural language descriptions of skills and their corresponding rollout data. This grounding process enhances the LLM's comprehension of environmental dynamics.
Rollout Generation: The fine-tuned LLM is then used to generate diverse and meaningful imaginary rollouts that reflect novel skills, including those that require unprecedented optimal behaviors.
Skill Acquisition: The offline RL training is conducted using both the original offline dataset and the LLM-generated imaginary rollouts, enabling the agent to acquire a broader set of skills.

The experiments on the CLEVR-Robot environment demonstrate the effectiveness of KALM. Compared to baseline offline RL methods, KALM achieves a significantly higher success rate (46% vs. 26%) on tasks with unseen natural language goals, showcasing its ability to generalize to novel situations. The results also highlight the LLM's capacity to comprehend environmental dynamics and generate meaningful imaginary rollouts that reflect novel skills, enabling the seamless integration of large language models and reinforcement learning.

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Статистика

KALM achieves a 46% success rate on tasks with unseen natural language goals, substantially surpassing the 26% success rate of baseline offline RL methods.
The LLM fine-tuned by KALM demonstrates an accuracy of over 90% in explaining both seen and unseen environmental rollouts.
The state generation accuracy of the LLM-generated rollouts for unseen (easy) tasks improves from 22% to 32% during the training process, while the action generation accuracy remains around 30%.

Цитати

"KALM implements this concept by grounding LLMs within the environment to generate rollouts of various skills, thereby facilitating skill acquisition via offline RL."
"The primary challenge of KALM lies in LLM grounding, as LLMs are inherently limited to textual data, whereas environmental data often comprise numerical vectors unseen to LLMs."
"Initial empirical evaluations on the CLEVR-Robot environment demonstrate that KALM enables agents to complete complex rephrasings of task goals and extend their capabilities to novel tasks requiring unprecedented optimal behaviors."

Ключові висновки, отримані з

Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts

by Jing-Cheng P... о arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09248.pdf

Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts

Глибші Запити

How can KALM be extended to handle more complex environments, such as those with visual observations or continuous action spaces?

To extend KALM to handle more complex environments, such as those with visual observations or continuous action spaces, several modifications and enhancements can be implemented:

Incorporating Vision Encoders: For environments with visual observations, integrating vision encoders into the LLM architecture can enable the model to process visual data. By incorporating convolutional neural networks (CNNs) or other vision models, the LLM can interpret images and extract relevant features for generating rollouts.

Multi-Modal Inputs: Adapting the LLM to accept multi-modal inputs, including text, images, and numerical data, can enhance its understanding of diverse environmental information. By incorporating different input modalities, the LLM can generate more comprehensive and contextually rich rollouts.

Continuous Action Spaces: To handle environments with continuous action spaces, the LLM architecture can be modified to output continuous action values rather than discrete actions. This adjustment allows the model to generate precise and continuous action sequences, enabling agents to navigate environments with fine-grained control.

Hybrid Architectures: Implementing hybrid architectures that combine LLMs with specialized models designed for specific tasks, such as reinforcement learning with continuous action spaces, can further enhance the model's performance in complex environments. By leveraging the strengths of different models, KALM can adapt to a wider range of environments and tasks.

How might the integration of KALM with other offline RL algorithms or multi-modal models impact the overall performance and generalization capabilities of the learned policies?

Integrating KALM with other offline RL algorithms or multi-modal models can have several benefits for the overall performance and generalization capabilities of the learned policies:

Enhanced Skill Acquisition: Combining KALM with diverse offline RL algorithms can leverage the strengths of each approach, leading to more robust skill acquisition. By integrating multiple algorithms, the agent can learn from a broader range of data sources and improve its performance across various tasks.

Improved Generalization: The integration of multi-modal models with KALM can enhance the model's ability to generalize to different types of data inputs. By incorporating visual, textual, and numerical information, the model can develop a more comprehensive understanding of the environment, leading to improved generalization to novel tasks and scenarios.

Increased Adaptability: By combining KALM with other offline RL algorithms or multi-modal models, the learned policies can become more adaptable to diverse environments and tasks. The integration of different techniques can provide complementary benefits, enhancing the agent's adaptability and performance in complex settings.

Efficient Training: Integrating KALM with efficient offline RL algorithms can streamline the training process and improve the convergence speed of the learned policies. By leveraging the strengths of different algorithms, the training process can be optimized for faster and more effective policy learning.

Overall, the integration of KALM with other offline RL algorithms or multi-modal models can lead to synergistic effects, enhancing the performance, generalization capabilities, and adaptability of the learned policies in diverse and complex environments.