Offline Action Selection with Conditional Probability Ratios
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Solution Overview
Problem
Existing machine learning methods for offline action selection, such as contextual bandits and reinforcement learning for Markov Decision Problems, face challenges with high variance in multi-step processes, requiring large datasets and computational resources, and suffer from stability issues.
Innovation Solution
A machine learning method involving training two action prediction models, one with a desired outcome subset and one with the entire dataset, to compute a pairwise ratio of action selection probabilities, optimizing the probability of achieving a desired outcome while minimizing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If reinforcement learning for full Markov Decision Problems is applied to overcome limitations of contextual bandits, then the ability to handle multi-step processes is improved, but the variance of outcomes increases requiring huge datasets
Solution Approach 1:
The patent segments the action selection process into two independent prediction models: one that predicts action probabilities under the assumption of desired outcome, and another that predicts unconditional action probabilities. This segmentation allows each model to focus on specific aspects of the decision problem, reducing the variance in outcomes by separating the estimation of conditional and unconditional probabilities.
Solution Approach 2:
The patent introduces an intermediary ratio computation step that combines the outputs of the two prediction models. By computing the ratio of conditional to unconditional probabilities, the system creates a stable intermediary representation that reduces variance while maintaining the ability to handle multi-step processes.
2Reliability
If huge datasets are used to obtain reliable estimates of policy performance, then the reliability of policy estimation is improved, but memory and runtime requirements increase
Solution Approach 1:
The patent segments the data processing into two separate prediction models that can be trained independently on the same historical dataset. This segmentation allows for more efficient use of computational resources and reduces the effective data processing burden while maintaining reliable policy estimation through the ratio computation.
Solution Approach 2:
The patent changes the parameter space by transforming the problem from direct multi-step outcome prediction to a ratio of two probability predictions. This parameter transformation allows reliable estimation with smaller effective datasets by changing how the probability estimates are computed and combined.
3Productivity
If reinforcement learning for full Markov Decision Problems is applied, then the ability to optimize total reward is improved, but computational resources and runtime increase
Solution Approach 1:
The patent segments the computational task into two independent prediction model training processes that can run in parallel, followed by a simple ratio computation for action selection. This segmentation significantly reduces runtime compared to traditional reinforcement learning while maintaining the ability to optimize total reward through the learned action probability ratios.
Solution Approach 2:
The patent substitutes the complex mechanical reinforcement learning process with a simpler two-model prediction approach. By replacing the iterative reinforcement learning loops with direct probability prediction and ratio computation, the system achieves comparable reward optimization with reduced computational runtime.
4Device complexity
If contextual bandit model is used for offline action selection, then the simplicity of single-step processing is maintained, but the ability to handle multi-step processes is limited
Solution Approach 1:
The patent segments the prediction task into two specialized models: one for conditional probability estimation (action probability given desired outcome) and one for unconditional probability estimation. This segmentation enables the system to handle multi-step processes by combining the outputs of both models, while maintaining the simplicity of independent model training and a straightforward ratio computation.
Data Source
AI summary
A method for action determination includes training a first action prediction model with a desired outcome subset of a dataset having triples of: state; actions; and outcome. The first prediction model is configured to receive an input state and to output an action selection probability, which represents a prediction of a probability of achieving a first desired outcome for each action. The method further includes training a second action prediction model with the entire dataset to minimize a total loss over all actions. The second action prediction model is configured to receive the input state and to output an unconditional action selection probability of taking each action for the input state. The method can be practically applied to machine learning and artificial intelligence use cases including, but not limited to, medical/healthcare, email filtering, speech recognition, and computer vision, to optimize processes or support decision making.