Convex Markov Decision Process Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing reinforcement learning algorithms struggle to converge to an optimal solution for specific convex cost functions, limiting their flexibility and accuracy in task performance, especially when designing a convex function is easier than a reward function.
Innovation Solution
The system employs a policy model and cost model neural network trained using a Lagrangian function based on a convex function, allowing for the derivation of an optimal state-action distribution that minimizes any convex function, thereby enhancing learning performance and flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing reinforcement learning algorithms are used, then training can be performed with standard reward functions, but the system cannot converge to optimal solutions for specific convex cost functions and lacks flexibility
Solution Approach 1:
The patent transforms the reinforcement learning problem by changing the mathematical parameters and formulation. Instead of using standard reward maximization, it reformulates the problem as convex cost minimization with properly defined convex functions, allowing the system to handle a broader class of cost functions while guaranteeing convergence to optimal solutions through convex optimization theory
Solution Approach 2:
The patent replaces the traditional trial-and-error reinforcement learning mechanism with a systematic convex optimization approach. By substituting the exploratory RL training process with a mathematically guaranteed convex minimization framework, the system achieves both flexibility in cost function design and reliability in convergence
2Measurement precision
If standard reinforcement learning methods are used, then implementation is straightforward, but learning performance and accuracy are limited
Solution Approach 1:
The patent applies preliminary action by pre-defining proper convex cost functions and verifying their convexity properties before training begins. This preliminary setup ensures that the optimization landscape is well-formed, enabling higher learning accuracy while maintaining manageable system complexity through mathematically sound foundations
3Productivity
If existing RL algorithms are used, then training can proceed with standard approaches, but computational requirements increase and certain tasks cannot be solved
Solution Approach 1:
The patent extracts and isolates the convex cost functions from the general reinforcement learning framework, treating them as separate, well-defined mathematical objects. This extraction allows the system to solve previously unsolvable tasks by focusing computational resources on properly formulated convex optimization problems rather than attempting to solve all RL problems with generic algorithms
Data Source
AI summary
The actions of an agent in an environment are selected using a policy model neural network which implements a policy model defining, for any observed state of the environment characterized by an observation received by the policy model neural network, a state-action distribution over the set of possible actions the agent can perform. The policy model neural network is jointly trained with a cost model neural network which, upon receiving an observation characterizing the environment, outputs a reward vector. The reward vector comprises a corresponding reward value for every possible action. The training involves a sequence of iterations, in each of which (a) a cost model is derived based on the state-action distribution of a candidate policy model defined in one or more previous iterations, and subsequently (b) a candidate policy model is obtained based on reward vector(s) defined by the cost model obtained in the iteration.