Neural Network CFR for Imperfect Information Games
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Solution Overview
Problem
Existing methods for solving Imperfect Information Games (IIGs) face challenges in reducing computational complexity and variance, particularly in large games with limited memory, as they require extensive game tree traversal and large memory usage, making them inefficient for real-world applications like resource allocation and cyber-attack prediction.
Innovation Solution
The implementation of sampling schemes such as robust sampling and depth-dependent sampling for the Counterfactual Regret Minimization (CFR) algorithm, combined with the use of neural networks to reduce memory requirements and improve convergence speed, allowing for more efficient strategy searching in IIGs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Counterfactual Regret Minimization (CFR) algorithm is used to solve Imperfect Information Games, then solution accuracy is improved, but computational complexity and memory usage increase significantly
Solution Approach 1:
The patent segments the game tree traversal into independent sampling units. Instead of traversing the entire game tree, the algorithm samples individual trajectories or subsets of the game tree, dividing the computational task into manageable segments that can be processed independently and in parallel.
Solution Approach 2:
The patent extracts only the necessary information from the game tree by sampling specific trajectories relevant to the current iteration. This extraction approach retrieves only the critical data needed for regret calculation, avoiding the overhead of processing the complete game tree structure.
2Measurement precision
If traditional CFR algorithm traverses the entire game tree, then convergence accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by sampling only a subset of game tree trajectories rather than exhaustively traversing the entire tree. This partial traversal provides sufficient information for accurate regret estimation while dramatically reducing processing time, achieving the principle of doing enough rather than everything.
Solution Approach 2:
The algorithm implements periodic sampling where trajectories are selected at regular intervals or according to periodic patterns. This periodic approach ensures systematic coverage of the game tree over multiple iterations while maintaining efficiency in each individual iteration.
3Measurement precision
If large memory is allocated for storing game tree information, then solution precision is improved, but memory efficiency deteriorates
Solution Approach 1:
The patent extracts only the essential data elements needed for CFR computation from the game tree, storing merely the sampled trajectories and associated regret values. This extraction eliminates the need to store redundant game tree structures, achieving high solution precision with minimal memory consumption.
Solution Approach 2:
Instead of storing the complete game tree structure, the algorithm creates simplified copies or representations of only the sampled trajectories. These lightweight copies contain the necessary information for regret calculation without the overhead of the full game tree, enabling efficient memory usage.
4Stability of the object's composition
If comprehensive game tree information is processed, then variance reduction is improved, but computational load increases
Solution Approach 1:
The patent performs preliminary sampling actions that strategically select trajectories likely to provide informative regret estimates. By pre-selecting promising trajectories based on heuristics or previous iterations, the algorithm reduces variance in the regret calculation without requiring exhaustive processing of all possible paths.
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing counterfactual regret minimization (CFR) for strategy searching in strategic interaction between two or more parties. One of the methods includes: storing multiple regret samples in a first data store, wherein the multiple regret samples are obtained in two or more iterations of a CFR algorithm in strategy searching in strategic interaction between two or more parties; storing multiple strategy samples in a second data store; updating parameters of a first neural network for predicting a regret value of a possible action in a state of a party based on the multiple regret samples in the first data store; and updating parameters of a second neural network for predicting a strategy value of a possible action in a state of the party based on the multiple strategy samples in the second data store.