Streamline CFR Algorithm for Memory-Constrained Imperfect Information Games
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Solution Overview
Problem
Existing methods for solving Imperfect Information Games (IIGs) face challenges with high memory requirements and slow convergence due to the need to traverse entire game trees, making them inefficient for large-scale games.
Innovation Solution
The streamline Counterfactual Regret Minimization (CFR) algorithm reduces memory usage by half and improves convergence speed by only tracking key information with a single neural network, allowing it to handle large games with memory constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the original CFR algorithm traverses the entire game tree to solve IIGs, then the solution accuracy is maintained, but the memory requirements become prohibitively high and convergence speed becomes slow
Solution Approach 1:
The patent segments the game tree traversal into sampled trajectories rather than exhaustive exploration. The CFR algorithm is modified to process only selected game paths (trajectories) at each iteration, dividing the computational workload into manageable segments that fit within memory constraints while still providing accurate Nash equilibrium solutions through sufficient sampling across multiple iterations.
Solution Approach 2:
The patent applies partial action by processing only a subset of the game tree (sampled trajectories) rather than the complete tree. This partial traversal reduces memory requirements significantly while maintaining solution accuracy through repeated sampling and aggregation of regret values across multiple iterations, effectively using 'less than full' computation to achieve the same analytical result.
2Reliability
If the original CFR algorithm traverses the entire game tree, then comprehensive game analysis is achieved, but the convergence speed becomes slow
Solution Approach 1:
The patent implements periodic action through iterative sampling cycles. Instead of exhaustive single-pass traversal, the algorithm performs multiple iterations of sampled trajectory processing, periodically updating regret values and strategies. This periodic re-sampling of game trajectories accelerates convergence by efficiently aggregating information across iterations while maintaining comprehensive game analysis through sufficient coverage of the game space.
Solution Approach 2:
The patent applies preliminary action by pre-selecting and sampling important game trajectories before full processing. The algorithm identifies and prioritizes relevant game paths in advance, processing these sampled trajectories first to establish initial regret estimates, which then guide subsequent iterations. This preliminary sampling accelerates convergence by focusing computational resources on the most informative game paths.
3Reliability
If traditional methods are used to solve large-scale IIGs, then theoretical completeness is maintained, but computational efficiency becomes unacceptably low
Solution Approach 1:
The patent substitutes the mechanical exhaustive traversal system with a probabilistic sampling system. Instead of systematically processing every node in the game tree (mechanical approach), the algorithm uses randomized trajectory sampling combined with regret minimization principles. This substitution maintains theoretical completeness through sufficient sampling laws while dramatically improving computational efficiency by avoiding processing of redundant or less informative game paths.
Data Source
AI summary
Disclosed herein are methods, systems, and apparatus of an execution device for generating an action selection policy for completing a task in an environment that includes the execution device and one or more other devices. One method includes: in a current iteration, identifying an iterative action selection policy of an action in a state of the execution device in a previous iteration; computing a regret value in the previous iteration based on the iterative action selection policy in the previous iteration; computing an incremental action selection policy in the current iteration based on the regret value in the previous iteration but not any regret value in any iteration prior to the previous iteration; computing an iterative action selection policy in the current iteration based on the iterative action selection policy in the previous iteration and the incremental action selection policy in the current iteration.