Dell products l.p. (20240249149). WARM UP TABLE FOR FAST REINFORCEMENT LEARNING MODEL TRAINING simplified abstract
Contents
WARM UP TABLE FOR FAST REINFORCEMENT LEARNING MODEL TRAINING
Organization Name
Inventor(s)
Eduardo Vera Sousa of Niterói (BR)
João Victor Pinto of Rio de Janeiro (BR)
Julia Drummond Noce of Rio de Janeiro (BR)
[[:Category:Micael Veríssimo De Ara�jo of Rio de Janeiro (BR)|Micael Veríssimo De Ara�jo of Rio de Janeiro (BR)]][[Category:Micael Veríssimo De Ara�jo of Rio de Janeiro (BR)]]
Yanexis Pupo Toledo of Rio de Janeiro (BR)
WARM UP TABLE FOR FAST REINFORCEMENT LEARNING MODEL TRAINING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240249149 titled 'WARM UP TABLE FOR FAST REINFORCEMENT LEARNING MODEL TRAINING
Simplified Explanation: The patent application describes the generation of warm-up or look-up tables for training reinforcement learning models. These tables include a probability distribution of a relevant metric, allowing for the determination of rewards without waiting for a workload to finish executing.
Key Features and Innovation:
- Warm-up tables are generated to expedite the reward determination process in training reinforcement learning models.
- Probability distributions of relevant metrics are included in the tables to enable quicker reward calculation.
- Averages and standard deviations of different workload instance-device associations are considered in the warm-up table generation process.
Potential Applications: This technology can be applied in various fields such as robotics, autonomous systems, and game playing algorithms where reinforcement learning models are utilized.
Problems Solved: The technology addresses the challenge of waiting for workload execution times to determine rewards in training reinforcement learning models, thereby shortening training times.
Benefits:
- Faster reward determination leads to shorter training times.
- Helps in compensating for the exploration/exploitation trade-off in training reinforcement learning models.
Commercial Applications: Potential commercial applications include optimizing resource allocation in cloud computing, enhancing autonomous systems' decision-making processes, and improving game playing algorithms' performance.
Prior Art: Readers can explore prior research on reinforcement learning, warm-up strategies, and reward determination methods in training models to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in reinforcement learning algorithms, warm-up techniques, and optimization strategies for training models to enhance the application of this technology.
Questions about Warm-Up Tables: 1. How do warm-up tables improve the efficiency of training reinforcement learning models? 2. What are the key considerations in generating warm-up tables for different workload instance-device associations?
Original Abstract Submitted
warm up or look up tables are generated for training reinforcement learning models. rather than wait for a metric, such as execution times, that are required to determine a reward, previously generated warm up tables that include a probability distribution of the metric are used such that the reward can be determined without waiting for a workload to finish executing. the ability to determine the reward more quickly can shorten training times and help compensate for the exploration/exploitation trade-off experienced in training reinforcement learning models. the warm up table considers averages of a relevant metric and standard deviation of different workload instance-device associations such that the metric can be sampled from the probability distribution.
