LLM-Generated Reward Functions for RL Risk Prediction
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Solution Overview
Problem
Existing systems face challenges in designing effective reward functions for reinforcement learning agents to accurately predict risky events in electronic marketplaces, due to complexities in aligning short-term gains with long-term objectives, avoiding unintended behaviors, and adapting to dynamic environments, leading to inefficient resource use and suboptimal performance.
Innovation Solution
Utilizing a large language model (LLM) to generate and refine reward functions for reinforcement learning agents, enabling faster and more efficient training by iteratively improving the reward functions based on performance scores and ground truth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual design and iteration of reward functions is used, then the reinforcement learning agent can be trained to predict risky events, but the process consumes excessive computational resources and time
Solution Approach 1:
The patent introduces an intermediary system that automatically generates and optimizes reward functions based on task descriptions and ground truth data. This intermediary layer eliminates the need for manual reward function design and iterative tuning, thereby improving training efficiency while maintaining prediction accuracy through automated optimization processes.
Solution Approach 2:
The system enables self-service by allowing the reinforcement learning framework to automatically generate and refine its own reward functions without external intervention. The automated system iteratively improves reward functions by evaluating performance against ground truth data, reducing human resource consumption while maintaining reliable predictions.
2Reliability
If complex reward functions are designed to capture long-term objectives, then the agent can avoid unintended behaviors, but the design process becomes increasingly difficult and resource-intensive
Solution Approach 1:
The patent implements feedback mechanisms where the system automatically evaluates the reinforcement learning agent's performance against ground truth data and uses this feedback to iteratively optimize reward functions. This automated feedback loop ensures that complex long-term objectives are properly captured without requiring manual design of complicated reward structures, as the system self-adjusts based on performance evaluation.
3Measurement precision
If extensive manual iteration is performed to optimize reward functions, then the agent achieves higher prediction accuracy, but the time and computational resources required increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-defining task descriptions and ground truth data that guide the automated reward function generation process. This preliminary setup enables the system to directly generate optimized reward functions without requiring extensive manual iteration, thereby achieving high prediction accuracy while significantly reducing the time and computational resources needed compared to traditional manual tuning approaches.
Data Source
AI summary
Various examples described herein support or provide operations including providing a prompt to a large language model (LLM) for generating reward functions. The prompt can include a set of instructions for generating a set of reward functions associated with training a reinforcement learning (RL) agent to predict an objective. The set of reward functions is obtained from the LLM and used to train one or more instances of an RL agent to predict the objective. A score representing accuracy of the predicted objective for the one or more instances of the RL agent is generated and an individual instance of the one or more instances of the RL agent is selected to predict the objective based on the generated score.