Stochastic Action Set MDP Policy Gradient
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Solution Overview
Problem
Conventional Markov decision processes (MDPs) fail to account for stochastic action sets, leading to suboptimal decision-making in real-world scenarios where action availability is probabilistic, resulting in inaccurate recommendations and reduced usability.
Innovation Solution
A stochastic action set-based MDP model is developed, using a policy gradient that accounts for the stochasticity of action availability, enabling the computation of a policy function and selection of actions based on probability distributions, thereby adapting to changing action sets over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a conventional MDP with a static action set is used, then the model structure is simple and easy to implement, but the decision-making accuracy deteriorates when action availability is stochastic
Solution Approach 1:
The patent transforms the static action set into a dynamic stochastic action set where action availability is modeled as a probabilistic function of the current state. The policy function adapts to changing action availability by computing action probabilities based on both state and available actions, allowing the model to dynamically adjust to stochastic environments without requiring complex restructuring.
Solution Approach 2:
The patent modifies the MDP formulation by introducing a stochastic action set parameterized by availability probabilities. The policy gradient method incorporates these probabilistic parameters directly into the optimization objective, allowing the system to learn optimal policies that account for action unavailability without fundamentally changing the underlying MDP structure.
2Measurement precision
If the action set is made stochastic to reflect real-world conditions, then the decision-making accuracy improves, but the model complexity increases
Solution Approach 1:
The patent creates a universal policy function that handles both deterministic and stochastic action sets through a unified probabilistic framework. The same policy gradient algorithm works whether actions are always available or stochastically available, eliminating the need for separate handling mechanisms and reducing overall system complexity despite the stochastic formulation.
Solution Approach 2:
The patent incorporates feedback from the stochastic action set into the policy gradient computation. The availability of actions directly influences the gradient updates, allowing the policy to learn from the stochastic environment and adapt its behavior accordingly. This feedback mechanism enables accurate decision-making while maintaining a relatively simple model structure through efficient gradient-based optimization.
3Productivity
If actions are selected based on deterministic rules, then the selection process is simple and fast, but the adaptability to changing action availability deteriorates
Solution Approach 1:
The patent replaces deterministic action selection with a dynamic probabilistic selection process. The policy function computes action probabilities based on the current state and available actions, allowing the system to adapt to changing action availability in real-time. This dynamic approach maintains computational efficiency through direct probability sampling while significantly improving adaptability to stochastic environments.
4Productivity
If recommended actions are generated without considering action availability, then the computation is faster, but the usability and reliability of recommendations deteriorates
Solution Approach 1:
The patent performs preliminary filtering of the action set by identifying which actions are currently available before generating recommendations. This preliminary step ensures that only feasible actions are considered in the policy computation, preventing the generation of unreliable recommendations while maintaining computational efficiency through focused optimization over the available action subset.
Data Source
AI summary
Systems and methods are described for a decision-making process including actions characterized by stochastic availability, provide an Markov decision process (MDP) model that includes a stochastic action set based on the decision-making process, compute a policy function for the MDP model using a policy gradient based at least in part on a function representing the stochasticity of the stochastic action set, identify a probability distribution for one or more actions available at a time period using the policy function, and select an action for the time period based on the probability distribution.