Reinforcement Learning Reward Modeling With VLM Feedback
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Solution Overview
Problem
In reinforcement learning, improper design of reward functions leads to agents failing to learn a correct or optimal policy, especially in complex scenarios, due to overly complicated or over-simplified reward functions.
Innovation Solution
A method involving a visual-language model (VLM) as the reward function, combined with a learnable network in the reward model, where weight parameters are adjusted to improve the accuracy of the reward function, ensuring the policy parameters learned are accurate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a reward function is manually designed by research and development personnel for various usage scenarios, then the reward function can be customized for specific tasks, but the design becomes more complicated for complex application scenarios and may result in improper design leading to incorrect or suboptimal policies
Solution Approach 1:
The system enables self-service by allowing the agent to automatically learn and optimize the reward function through interaction with the environment. The reward function is not manually designed but emerges through the learning process, where the agent autonomously determines optimal policies based on environmental feedback, eliminating the need for complex manual design while maintaining adaptability across scenarios
Solution Approach 2:
The patent applies parameter changes by dynamically adjusting reward parameters during the learning process. Instead of fixing the reward function design manually, the system allows reward parameters to evolve and adapt based on environmental interactions and learning outcomes, enabling the reward function to automatically adjust to different scenarios without increasing design complexity
2Productivity
If a pre-adjusted reward model is used in reinforcement learning, then the learning process can proceed with a fixed reward function, but the reward function may not accurately describe the task process leading to inaccurate policy parameters
Solution Approach 1:
The system implements dynamics by transitioning from a static, pre-adjusted reward model to a dynamic reward function that continuously adapts during the learning process. The reward function evolves based on environmental feedback and learning progress, allowing it to accurately describe the task process at each stage while maintaining learning efficiency through automated adaptation
Solution Approach 2:
The patent applies feedback by using environmental outcomes and learning progress to continuously refine the reward function. The system incorporates feedback loops where the agent's interactions with the environment provide information that adjusts the reward parameters, ensuring the reward function accurately reflects the task process while maintaining efficient learning through data-driven optimization
Data Source
AI summary
Disclosed are a method, apparatus, and electronic device for training a reinforcement learning model, relating to the field of computer vision, the method includes determining a task instruction for instructing an agent to perform a target task; determining a plurality of data information sets and a plurality of first state images generated during the performing of the target task by the agent; adjusting weight parameters for a reward model based on the task instruction and the plurality of first state images; and adjusting policy parameters for a first reinforcement learning model based on the adjusted the reward model and the plurality of data information set.