Deepmind technologies limited (20240220774). DEEP REINFORCEMENT LEARNING WITH FAST UPDATING RECURRENT NEURAL NETWORKS AND SLOW UPDATING RECURRENT NEURAL NETWORKS simplified abstract
DEEP REINFORCEMENT LEARNING WITH FAST UPDATING RECURRENT NEURAL NETWORKS AND SLOW UPDATING RECURRENT NEURAL NETWORKS
Organization Name
Inventor(s)
Iain Robert Dunning of New York NY (US)
Wojciech Czarnecki of London (GB)
Maxwell Elliot Jaderberg of London (GB)
DEEP REINFORCEMENT LEARNING WITH FAST UPDATING RECURRENT NEURAL NETWORKS AND SLOW UPDATING RECURRENT NEURAL NETWORKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240220774 titled 'DEEP REINFORCEMENT LEARNING WITH FAST UPDATING RECURRENT NEURAL NETWORKS AND SLOW UPDATING RECURRENT NEURAL NETWORKS
The patent application describes methods, systems, and apparatus for reinforcement learning, including computer programs encoded on computer storage media. One method involves selecting an action for the agent using both a slow updating recurrent neural network and a fast updating recurrent neural network that receives a fast updating input containing the hidden state of the slow updating recurrent neural network.
- Utilizes both slow updating and fast updating recurrent neural networks for reinforcement learning.
- The fast updating network receives input from the hidden state of the slow updating network.
- Computer programs are encoded on computer storage media for implementation.
- Focuses on optimizing the selection of actions for the agent in reinforcement learning scenarios.
- Aims to improve the efficiency and effectiveness of reinforcement learning algorithms.
Potential Applications: - Autonomous vehicles - Robotics - Gaming industry for AI opponents - Financial trading algorithms - Healthcare for personalized treatment plans
Problems Solved: - Enhancing decision-making processes in complex environments - Improving the learning capabilities of AI systems - Addressing the need for efficient reinforcement learning algorithms
Benefits: - Increased accuracy in decision-making - Faster learning and adaptation to changing environments - Enhanced performance in various applications - Potential cost savings through optimized processes
Commercial Applications: Title: "Optimized Reinforcement Learning Algorithms for Enhanced Decision-Making" This technology can be applied in industries such as autonomous vehicles, robotics, gaming, finance, and healthcare to improve decision-making processes and optimize outcomes. The market implications include increased efficiency, accuracy, and competitiveness for businesses utilizing these advanced algorithms.
Questions about Reinforcement Learning: 1. How does the integration of slow and fast updating recurrent neural networks improve the performance of reinforcement learning algorithms? 2. What are the potential limitations or challenges associated with implementing this technology in real-world applications?
Frequently Updated Research: Stay updated on recent advancements in reinforcement learning algorithms, neural network optimization, and applications in various industries to enhance your understanding of this evolving technology.
Original Abstract Submitted
methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning. one of the methods includes selecting an action to be performed by the agent using both a slow updating recurrent neural network and a fast updating recurrent neural network that receives a fast updating input that includes the hidden state of the slow updating recurrent neural network.
