Deepmind technologies limited (20240256883). REINFORCEMENT LEARNING USING QUANTILE CREDIT ASSIGNMENT simplified abstract
REINFORCEMENT LEARNING USING QUANTILE CREDIT ASSIGNMENT
Organization Name
Inventor(s)
Mark Daniel Rowland of London (GB)
Theophane Guillaume Weber of London (GB)
Wenqi Chen of Cambridge MA (US)
REINFORCEMENT LEARNING USING QUANTILE CREDIT ASSIGNMENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240256883 titled 'REINFORCEMENT LEARNING USING QUANTILE CREDIT ASSIGNMENT
Simplified Explanation: The patent application describes methods, systems, and apparatus for training a neural network to select actions for an agent interacting with an environment, taking into account external factors such as luck.
Key Features and Innovation:
- Training a neural network to select actions for an agent in an environment with a level of luck.
- Accounting for outcomes caused by external factors during the learning process.
- Enhancing the decision-making process of the agent by considering both internal and external factors.
Potential Applications: This technology could be applied in various fields such as autonomous vehicles, robotics, gaming, and financial trading where decision-making in uncertain environments is crucial.
Problems Solved: The technology addresses the challenge of decision-making in environments with unpredictable outcomes influenced by external factors beyond the agent's control.
Benefits:
- Improved decision-making capabilities in uncertain environments.
- Enhanced adaptability to varying conditions.
- Increased efficiency and effectiveness of actions taken by the agent.
Commercial Applications: Potential commercial applications include autonomous vehicles, smart manufacturing systems, gaming AI, and financial trading algorithms.
Prior Art: Readers can start their search for prior art related to this technology by exploring patents in the fields of artificial intelligence, machine learning, and decision-making algorithms.
Frequently Updated Research: Stay updated on the latest research in neural network training, reinforcement learning, and decision-making algorithms to further enhance the technology's capabilities.
Questions about Neural Network Training for Decision-Making: 1. How does the technology differentiate between outcomes influenced by luck and those caused by the agent's actions? 2. What are the potential limitations of training a neural network to account for external factors in decision-making processes?
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a neural network used to select actions to be performed by an agent interacting with an environment. implementations of the system can take into account a level of luck in the environment, and hence whilst learning can account for outcomes that were caused by external factors as well as those dependent on the actions of the agent.
