AI Redefined Inc. (20240249198). SYSTEMS AND METHODS FOR REAL-TIME REINFORCEMENT LEARNING simplified abstract
Contents
SYSTEMS AND METHODS FOR REAL-TIME REINFORCEMENT LEARNING
Organization Name
Inventor(s)
François Chabot of Montreal (CA)
SYSTEMS AND METHODS FOR REAL-TIME REINFORCEMENT LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240249198 titled 'SYSTEMS AND METHODS FOR REAL-TIME REINFORCEMENT LEARNING
Simplified Explanation: The patent application describes systems and methods for deferring the aggregation of rewards in reinforcement learning while maintaining live-learning capabilities. It allows retroactive rewards from human operators to be incorporated into online learning processes without significant alterations, using a sliding-time window approach.
- Rewards aggregation deferred in reinforcement learning
- Retroactive rewards from human operators integrated into online learning processes
- Sliding-time window used to accumulate and dispatch rewards
- Minimizes the use of fast-access computer memory
- Maintains live-learning capabilities without substantial changes
Potential Applications: 1. Reinforcement learning systems 2. Online learning platforms 3. Interactive training programs
Problems Solved: 1. Integrating retroactive rewards into online learning processes 2. Minimizing the use of fast-access computer memory 3. Maintaining live-learning capabilities in reinforcement learning
Benefits: 1. Enhanced learning outcomes 2. Improved efficiency in reward aggregation 3. Seamless integration of human feedback
Commercial Applications: The technology could be applied in educational software, training simulations, and AI-driven decision-making systems, enhancing their learning capabilities and performance.
Prior Art: Researchers can explore existing patents and academic papers on reinforcement learning, reward aggregation, and online learning processes to understand the prior art related to this technology.
Frequently Updated Research: Stay informed about advancements in reinforcement learning algorithms, online learning methodologies, and human-in-the-loop systems to enhance the understanding and implementation of this technology.
Questions about the Technology: 1. How does the sliding-time window approach optimize reward aggregation in reinforcement learning? 2. What are the potential implications of integrating retroactive rewards from human operators into online learning processes?
Original Abstract Submitted
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.
