MODEL-BASED REINFORCEMENT LEARNING

Organization Name

Inventor(s)

Kaushik Balakrishnan of Mountain View CA (US)

MODEL-BASED REINFORCEMENT LEARNING - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240320505 titled 'MODEL-BASED REINFORCEMENT LEARNING

The abstract describes a computer system that trains an agent neural network to input a state and output an action, then input that action into an environment to determine a new state and a reward. A Koopman model neural network can be trained to generate a fake state based on the input and output states, and the agent neural network can be re-trained based on reinforcement learning using the states, the fake state, and the reward.

The computer system includes a processor and memory.
The memory contains instructions for training an agent neural network.
The agent neural network inputs a state and outputs an action.
The action is input into an environment to determine a new state and a reward.
A Koopman model neural network generates a fake state based on the input and output states.
The agent neural network is re-trained based on reinforcement learning using the states, the fake state, and the reward.

Potential Applications: - Autonomous vehicles - Robotics - Gaming industry

Problems Solved: - Improving decision-making processes in AI systems - Enhancing the efficiency of neural network training

Benefits: - Increased accuracy in predicting outcomes - Enhanced performance of AI systems

Commercial Applications: Title: "Enhancing AI Decision-Making in Autonomous Systems" This technology can be utilized in autonomous vehicles, robotics, and the gaming industry to improve decision-making processes and enhance overall system performance. The market implications include increased efficiency, accuracy, and reliability in AI systems.

Questions about the technology: 1. How does this technology improve the decision-making process in AI systems? 2. What are the potential commercial applications of this technology?

Original Abstract Submitted

a computer that includes a processor and a memory, the memory including instructions executable by the processor to train an agent neural network to input a first state and output a first action, input the first action to an environment and determine a second state and a reward. koopman model neural network can be trained based on the first state, the first action and the second state to determine a fake state. the agent neural network can be re-trained and the koopman model neural network can be re-trained based on reinforcement learning including the first state, the first action, the second state, the fake state, and the reward.