arXiv:2408.16032v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have enabled understanding webpage contexts, product details, and human instructions. Utilizing LLMs as the foundational architecture for either reward models or policies in reinforcement learning has gained popularity — a notable achievement is the success of InstructGPT. RL algorithms have been instrumental in maximizing long-term customer satisfaction and avoiding short-term, myopic goals in industrial recommender systems, which often rely on deep learning models to predict immediate clicks or purchases. In this project, several RL methods are implemented and evaluated using the WebShop benchmark environment, data, simulator, and pre-trained model checkpoints. The goal is to train an RL agent to maximize the purchase reward given a detailed human instruction describing a desired product. The RL agents are developed by fine-tuning a pre-trained BERT model with various objectives, learning from preferences without a reward model, and employing contemporary training techniques such as Proximal Policy Optimization (PPO) as used in InstructGPT, and Direct Preference Optimization (DPO). This report also evaluates the RL agents trained using generative trajectories. Evaluations were conducted using Thompson sampling in the WebShop simulator environment. The simulated online experiments demonstrate that agents trained on generated trajectories exhibited comparable task performance to those trained using human trajectories. This has demonstrated an example of an extremely low-cost data-efficient way of training reinforcement learning agents. Also, with limited training time (
and resources), the RL agents trained on generative trajectories were able to achieve competitive performance. This research highlights the potential of utilizing large language models and RL algorithms in improving recommender systems and maximizing customer satisfaction in e-commerce settings.
Recent advancements in large language models (LLMs) have opened up new possibilities in understanding webpage contexts, product details, and human instructions. These LLMs serve as the foundational architecture for reward models or policies in reinforcement learning, with notable success seen in InstructGPT.
Reinforcement learning (RL) algorithms play a crucial role in maximizing long-term customer satisfaction and avoiding short-term, myopic goals in industrial recommender systems. These systems often rely on deep learning models to predict immediate clicks or purchases. However, RL methods offer a more holistic approach to decision-making.
In this project, RL methods are implemented and evaluated using the WebShop benchmark environment, along with relevant data, simulator, and pre-trained model checkpoints. The ultimate goal is to train an RL agent that can maximize the purchase reward, given a detailed human instruction describing the desired product.
To develop these RL agents, a pre-trained BERT model is fine-tuned with various objectives. The agents learn from preferences without a reward model and employ contemporary training techniques like Proximal Policy Optimization (PPO), as used in InstructGPT, and Direct Preference Optimization (DPO).
This report also evaluates the RL agents trained using generative trajectories. Evaluations are conducted using Thompson sampling in the WebShop simulator environment. Remarkably, the results demonstrate that agents trained on generated trajectories perform comparably to those trained using human trajectories.
This finding represents an example of an extremely low-cost and data-efficient approach to training reinforcement learning agents. With limited training time, significant progress can be made in developing RL agents that are capable of making intelligent purchase decisions based on human instructions.
This research has implications for the e-commerce industry, where RL agents can assist customers in finding their desired products more efficiently. It also highlights the potential for reducing reliance on expensive data collection methods by leveraging generative trajectories.
The success of utilizing LLMs in reinforcement learning paves the way for further exploration of these techniques in various domains. By leveraging contextual understanding and human instructions, RL agents can learn to make more informed decisions, ultimately improving user satisfaction and optimizing long-term goals.
e.g., a few hours), the RL agents were able to achieve satisfactory performance in maximizing the purchase reward.
One interesting aspect of this project is the use of large language models (LLMs) as the foundational architecture for reinforcement learning (RL) agents. LLMs have shown great potential in understanding webpage contexts, product details, and human instructions. By utilizing LLMs, the RL agents are able to comprehend the detailed human instructions describing the desired product, which is crucial for maximizing the purchase reward.
The authors have implemented and evaluated several RL methods in the WebShop benchmark environment. One notable approach is fine-tuning a pre-trained BERT model with various objectives. BERT, a state-of-the-art language model, provides a strong foundation for the RL agent’s understanding of the textual instructions. By fine-tuning BERT, the RL agent can adapt its understanding to the specific task of maximizing the purchase reward.
Furthermore, the authors explore learning from preferences without a reward model. This approach allows the RL agent to learn directly from human preferences, which can be a valuable alternative when a reward model is not available or difficult to define. This is particularly significant in real-world applications where designing a reward model can be challenging.
The report also mentions the use of contemporary training techniques such as Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO). PPO, a popular RL algorithm, has been successful in training models like InstructGPT. By leveraging these techniques, the RL agents can improve their performance and learn more efficiently from the available data.
One intriguing finding in the project is the evaluation of RL agents trained using generative trajectories. The simulated online experiments demonstrate that agents trained on generated trajectories perform comparably to those trained using human trajectories. This suggests that generating synthetic trajectories can be an effective and low-cost way of training RL agents, especially in scenarios where collecting human trajectories may be challenging or expensive.
Overall, this project showcases the potential of using large language models in reinforcement learning tasks. The combination of LLMs, fine-tuning techniques, and contemporary RL algorithms allows the RL agents to understand human instructions and maximize the purchase reward in the WebShop environment. The successful results achieved in a low-cost and data-efficient manner highlight the practicality and scalability of these approaches for real-world recommender systems.
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