Sliding Window Reward Aggregation for Reinforcement Learning
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Solution Overview
Problem
Human-in-the-loop reinforcement learning systems face challenges in assigning rewards in real-time due to human reaction times, leading to delays and inefficiencies in updating learning agent policies, especially when multiple evaluators provide feedback at different times.
Innovation Solution
Implementing a fixed length sliding window mechanism to collect and consolidate rewards for actions, allowing for immediate processing and aggregation of in-window rewards, and sending consolidated reward information to learning agents when actions are no longer within the window, thereby reducing the impact of delayed feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rewards are assigned in real-time to learning agent actions, then learning agent policy optimization is accelerated, but human reaction time delays cause rewards to be assigned late
Solution Approach 1:
The system pre-assigns rewards to learning agent actions based on preliminary evaluation, allowing the learning agent to immediately update its policy without waiting for complete human feedback. The reward assignment occurs in advance of the full evaluation process, enabling continuous learning while human evaluators work at their natural pace.
Solution Approach 2:
The reward assignment process is segmented into multiple stages: immediate preliminary reward assignment based on partial feedback, followed by subsequent adjustments when complete evaluation is available. This segmentation allows the learning agent to receive timely rewards for policy updates while accommodating the full evaluation timeline.
2Reliability
If multiple human evaluators provide feedback at different times, then comprehensive reward information is obtained, but the complexity of tracking and assigning rewards increases
Solution Approach 1:
A centralized reward management system acts as an intermediary between multiple human evaluators and learning agents. This intermediary consolidates feedback from multiple evaluators, tracks reward assignments systematically, and ensures accurate distribution of rewards to the appropriate learning agents, reducing the complexity burden on individual components.
Solution Approach 2:
Feedback from multiple human evaluators is merged into a unified reward assignment process. The system combines multiple evaluation streams into consolidated reward signals that are then assigned to learning agents, simplifying the tracking process by treating multiple evaluators as a single integrated feedback source.
3Speed
If rewards are stored in fast-access memory for immediate retrieval, then real-time reward assignment is enabled, but memory resource consumption increases
Solution Approach 1:
The system extracts only the essential reward information needed for immediate policy updates and stores it in fast-access memory, while archiving complete reward details in slower storage. This extraction approach enables real-time reward assignment with minimal memory resource consumption by storing only critical data in high-speed memory.
Solution Approach 2:
Essential reward information is pre-processed and stored in fast-access memory in advance, allowing immediate retrieval for real-time policy updates. Non-critical reward details are stored separately in slower storage, enabling the system to maintain high-speed reward assignment while minimizing fast-access memory usage.
Data Source
AI summary
Systems and methods for deferring aggregation of rewards while maintaining live-learning capabilities in reinforcement learning are described. The method provides for retroactive rewards from human operators to be available to online learning processes without requiring learning processes to be substantially altered, while minimizing the use of fast-access computer memory. The method makes use of a sliding-time window where retroactive rewards are accumulated before being dispatched to corresponding learning agents when time-points fall out of the window.