Reinforcement Learning Policy Optimization with Tradeoff Preferences
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Solution Overview
Problem
Reinforcement learning with multiple objectives poses challenges in quantifying and incorporating tradeoff preferences, as existing methods struggle to handle imprecise preferences and optimize actions across multiple objectives simultaneously.
Innovation Solution
A method and system for computing possibly optimal policies in reinforcement learning that receive state, action, and reward information, along with tradeoff information, to produce policies that balance multiple objectives by using a policy learner to calculate preliminary and finalized policies based on user input and tradeoff preferences, even when precise relationships between objectives are unknown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional reinforcement learning methods are used to handle multiple objectives, then the system can process multiple objectives simultaneously, but it struggles to incorporate imprecise tradeoff preferences and quantify preferences between objectives
Solution Approach 1:
The patent uses a disposable approximation approach by generating a set of candidate policies and selecting the best one based on imprecise preferences without requiring precise quantification of tradeoffs. The method accepts approximate solutions rather than seeking exact optimal policies, trading precision for practicality in handling multiple objectives with imprecise preferences
Solution Approach 2:
The patent transforms the problem by changing the parameter representation from precise numerical tradeoff weights to imprecise preference specifications. By allowing users to specify preferences in natural language or approximate terms rather than exact numerical weights, the system handles imprecision directly in the input parameters
2Reliability
If the system attempts to precisely quantify tradeoff preferences between objectives, then optimal policies can be determined, but the complexity of obtaining and processing precise preference information increases significantly
Solution Approach 1:
The patent applies partial action by not requiring complete and precise specification of all tradeoff preferences. Instead, it generates multiple candidate policies and selects the best one based on partial preference information, accepting that not all preferences need to be precisely quantified to achieve satisfactory results
Solution Approach 2:
The patent creates a universal framework that can handle both precise and imprecise preference specifications through the same policy generation and selection process. The system is designed to work with multiple types of input representations, making the complexity handling adaptable to different user needs
3Productivity
If existing methods optimize actions for multiple objectives simultaneously, then comprehensive coverage of all objectives is achieved, but the methods cannot effectively incorporate user preferences and tradeoff information
Solution Approach 1:
The patent segments the optimization process into two distinct phases: (1) generating multiple candidate policies that each optimize different combinations of objectives, and (2) selecting the best policy based on user preferences. This segmentation allows comprehensive objective coverage in the generation phase while making preference incorporation simple in the selection phase
Data Source
AI summary
A method for computing possibly optimal policies in reinforcement learning with multiple objectives and tradeoffs includes receiving a dataset comprising state, action, and reward information for objectives in a multiple objective environment. Tradeoff information indicating that a first vector comprising first values of the objectives in the multiple objective environment is preferred to a second vector comprising second values of the objectives in the multiple objective environment is received. A set of possibly optimal policies for the multiple objective environment is produced based on the dataset and the tradeoff information, where the set of possibly optimal policies indicates actions for an intelligent agent operating in the multiple objective environment to take.