18467351. METHOD FOR TRAINING AN AGENT simplified abstract (Robert Bosch GmbH)
Contents
- 1 METHOD FOR TRAINING AN AGENT
METHOD FOR TRAINING AN AGENT
Organization Name
Inventor(s)
Jelle Van Den Broek of Heiloo (NL)
Jan Guenter Woehlke of Leonberg (DE)
METHOD FOR TRAINING AN AGENT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18467351 titled 'METHOD FOR TRAINING AN AGENT
Simplified Explanation
The abstract describes a method for training an agent with a planning component by carrying out control passes and reducing a loss that includes an auxiliary loss for each coarse-scale state transition.
- Training method for an agent with a planning component
- Carries out control passes
- Reduces loss by training planning component
- Includes auxiliary loss for coarse-scale state transitions
- Represents deviation between planning component output and rewards received
Potential Applications
This technology could be applied in various fields such as robotics, autonomous vehicles, and game AI where agents need to make decisions based on planning and state transitions.
Problems Solved
1. Improving the efficiency and accuracy of agent decision-making processes. 2. Enhancing the learning capabilities of agents in complex environments with multiple state transitions.
Benefits
1. Increased performance and effectiveness of agents in dynamic environments. 2. Better adaptation to changing conditions and improved decision-making abilities.
Potential Commercial Applications
Optimizing resource allocation in logistics, enhancing route planning in transportation systems, and improving decision-making processes in financial trading algorithms.
Possible Prior Art
One possible prior art could be reinforcement learning techniques used in training agents with planning components, where loss functions are optimized to improve decision-making processes.
Unanswered Questions
How does this method compare to existing training techniques for agents with planning components?
This article does not provide a direct comparison with existing training techniques, leaving the reader to wonder about the specific advantages or disadvantages of this method in relation to others.
What are the specific real-world applications where this training method could have the most significant impact?
The article does not delve into specific examples or case studies of real-world applications where this training method could be particularly beneficial, leaving the reader curious about its practical implications in different industries.
Original Abstract Submitted
A method for training an agent having a planning component. The method includes carrying out a plurality of control passes, and training the planning component to reduce a loss that includes, for each of a plurality of coarse-scale state transitions occurring in the control passes from a coarse-scale state to a coarse-scale successor state, an auxiliary loss that represents a deviation between a value outputted by the planning component for the coarse-scale state and the sum of a reward received for the coarse-scale state transition and at least a portion of the value of the coarse-scale successor state.
