Imitation Learning Strategy Updates from Offline Teacher Preferences
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Solution Overview
Problem
Interactive imitation learning in reinforcement learning requires online feedback from a teacher model, limiting its applicability and necessitating a value function, which is challenging to obtain efficiently.
Innovation Solution
Generate an action or state value function based on offline data indicating preferences of the teacher model, enabling interactive imitation learning without real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If interactive imitation learning uses online feedback from a teacher model, then learning efficiency is improved, but the applicability is limited and device complexity increases
Solution Approach 1:
The patent pre-computes and stores value functions or policy models offline before actual learning begins. This preliminary action allows the system to use pre-generated guidance data during training, eliminating the need for real-time teacher model queries while maintaining learning efficiency. The offline pre-computed values serve as ready-to-use feedback mechanisms.
Solution Approach 2:
The patent creates a copy of the teacher model's value function or policy in an offline manner. Instead of requiring live access to the original teacher model, the system uses a pre-generated copy that contains the necessary guidance information. This copying approach enables the student model to learn from the teacher's expertise without establishing complex online feedback loops.
2Measurement precision
If interactive imitation learning requires a value function from the teacher model, then learning accuracy is improved, but obtaining the value function becomes more difficult
Solution Approach 1:
The system performs preliminary computation of value functions or policy models offline before the actual learning process. By pre-computing these critical components, the patent eliminates the difficulty of obtaining real-time value functions during training. The preliminary action stores the necessary accuracy information in a format that can be directly utilized without complex extraction processes.
Solution Approach 2:
The patent introduces an intermediary data structure (offline value function store or pre-computed policy model) that mediates between the teacher model and the student model. This intermediary contains the distilled guidance information in a format that is easy to access and process, bridging the gap between the original teacher model's complexity and the student model's learning requirements.
3Adaptability or versatility
If the system uses offline preference data instead of online feedback, then adaptability is improved, but the learning process becomes less efficient
Solution Approach 1:
The patent transforms the feedback mechanism from real-time online parameters to pre-computed offline parameters. By changing the temporal parameter of feedback delivery from synchronous to asynchronous, the system achieves greater adaptability while maintaining learning efficiency. The offline preference data is structured to preserve the essential learning signals needed for efficient training.
Solution Approach 2:
The system performs preliminary processing of preference data to extract and store valuable learning signals before actual training begins. This preliminary action transforms raw offline data into a format that can be efficiently consumed during learning, ensuring that the loss of real-time feedback does not significantly impact learning efficiency.
Data Source
AI summary
In a learning device, a generation means generates a value estimation model from preference data indicating combinations of each state and action. An acquisition means acquires a next state as an execution result of an action determined using a strategy of a learning target model. An estimation means estimates a state value or action value of a next state using the next state and the value estimation model. A strategy update means updates the strategy of the learning target model using the state value or the action value. Accordingly, it is possible to realize interactive imitation learning that can be performed with offline data indicating preferences for a teacher model.