Offline Preference-Based RL with Ranked Trajectory Lists
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Solution Overview
Problem
Existing reinforcement learning methods face challenges in designing effective reward functions, particularly in offline scenarios, where interaction with the environment is not possible, and there is a need for improved approaches to train reward models using human preference feedbacks.
Innovation Solution
An apparatus and method for offline preference-based reinforcement learning that constructs a ranked list of trajectories (RLT) based on preference feedbacks to sort trajectory segments and trains a reward model using preference pairs, allowing for effective training of the reward model even with a limited number of feedbacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If offline reinforcement learning is used to avoid interaction with the environment, then safety and efficiency are improved, but the ability to adapt to new situations deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-collecting diverse trajectory data from multiple sources (simulations, demonstrations, expert policies) before the actual reinforcement learning task. This offline data preparation enables the agent to learn from pre-acquired experiences without real-time environment interaction, thus ensuring safety while maintaining adaptability through rich pre-collected data
2Measurement precision
If human preference feedbacks are collected to train reward models, then learning accuracy is improved, but time consumption and feedback budget increase
Solution Approach 1:
The trajectory data is segmented into smaller trajectory segments that can be independently evaluated and combined. By segmenting the data collection and processing, the system can efficiently manage feedback budget while maintaining learning accuracy through structured combination of segmented preferences
Solution Approach 2:
The system transitions from pairwise trajectory comparison to listwise ranking by constructing Ranked Lists of Trajectories (RLT). This dimensional change from 2D (pairwise) to higher-dimensional (listwise) space enables more comprehensive preference representation without linearly increasing feedback requirements
3Ease of manufacture
If pairwise trajectory comparisons are used for preference feedback, then implementation simplicity is maintained, but information utilization efficiency deteriorates
Solution Approach 1:
The system merges multiple pairwise trajectory comparisons into a unified listwise ranking structure (RLT). By combining multiple pairwise preferences into a comprehensive ranked list, the system achieves efficient information utilization where the merged structure captures relative relationships among multiple trajectories simultaneously, improving productivity without sacrificing implementation simplicity
Data Source
AI summary
The embodiments disclosed herein are directed to a reinforcement learning apparatus and method. According to an embodiment, there is provided a reinforcement learning apparatus for performing offline preference-based reinforcement learning, the reinforcement learning apparatus including: memory configured to store a program and a dataset for performing reinforcement learning; and a controller provided with at least one processor, adapted to operate by executing the program stored in the memory, and configured to construct a ranked list of trajectories (RLT) by repeating the tasks of extracting a trajectory segment and adding the trajectory segment to the RLT, in which trajectory segments are sorted by preference level, based on preference feedbacks for a trajectory pair including the trajectory segment a plurality of times, and to train a reward model based on preference pairs each including two trajectory segments extracted from the RLT and a preference label assigned to the two trajectory segments.