Neural Network Training with Sequential Monte Carlo Search
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Solution Overview
Problem
Current machine learning techniques, such as Monte Carlo Tree Search, are inefficient and memory-intensive when solving complex problems, particularly in electrical circuit design, due to the large number of possible actions and outcomes, necessitating improved computational efficiency and scalability.
Innovation Solution
Utilizing a Sequential Monte Carlo process to train neural networks for decision-making, which involves generating simulations, updating weight values, and storing only simulation data, reducing memory usage and computational expense.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo Tree Search is used to solve complex problems, then solution accuracy is improved, but memory usage and computational expense increase significantly
Solution Approach 1:
The patent extracts and stores only the essential simulation data (states, actions, rewards, visit counts) in a buffer rather than maintaining the complete search tree structure in memory. This selective extraction of necessary information reduces memory consumption while preserving the ability to perform accurate searches and evaluations.
Solution Approach 2:
Instead of building and maintaining a traditional tree structure where parent-child relationships are explicitly stored, the patent inverts the approach by storing flat simulation records that can be re-evaluated. The tree structure is implicitly reconstructed from the stored simulation data when needed, rather than being maintained explicitly in memory.
2Measurement precision
If Monte Carlo Tree Search is used to solve complex problems, then solution accuracy is improved, but computational expense increases significantly
Solution Approach 1:
The patent performs preliminary actions by pre-generating and storing multiple simulations in a buffer before the actual search is executed. These pre-computed simulations contain valuable information about the search space that can be reused during the search process, reducing the need for redundant computations and lowering overall computational expense.
Solution Approach 2:
The patent creates copies of simulation data in a buffer that can be repeatedly accessed and re-evaluated without regenerating the simulations. This copying approach allows the system to perform multiple searches using the same underlying simulation data, significantly reducing computational expense compared to regenerating simulations each time.
3Adaptability or versatility
If the search space is expanded to cover all possible actions and outcomes, then solution completeness is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex search space into individual simulation records, each representing a specific trajectory through the state space. By dividing the overall search problem into manageable simulation segments that can be stored and processed independently, the system achieves comprehensive coverage without requiring a monolithic complex data structure.
4Measurement precision
If more simulations are generated to improve search accuracy, then solution quality is improved, but memory usage increases
Solution Approach 1:
The patent changes the parameter of how simulation data is stored and managed by using a buffer with fixed-size records that can be efficiently packed and managed. This parameter change allows the system to accommodate a larger number of simulations within the same memory footprint by optimizing the storage format and management strategy.
Data Source
AI summary
A computer-implemented method, a machine learning system, and non-transitory computer-readable storage medium for training a neural network are provided. The neural network is used to instruct an agent to select actions for interacting with an environment to determine a solution to a specified problem. In the computer-implemented method a state signal representing a current state of the environment is received. A Sequential Monte Carlo process is then used to perform a search to determine target action selection data associated with the current state of the environment. This target action selection data is stored in association with the state signal and the current state of the environment is updated by providing an action selection signal based on the target action selection data. The Sequential Monte Carlo process involves generating a plurality of simulations using the neural network to determine the target action selection data.