Latent Reward Function Estimation via PUR-IRL Algorithm
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Solution Overview
Problem
Current reinforcement learning systems face challenges in estimating latent reward functions and policies from experience data, particularly in complex environments like cancer evolution, where data is ambiguous and uncertain, and existing methods struggle to infer meaningful rewards or policies without explicit associations.
Innovation Solution
The proposed method involves generating a Markov Decision Process (MDP) to model agent interactions, initializing and updating partitions to assign experiences, and using a gradient update rule to refine latent reward functions, with the PUR-IRL algorithm employing a Bayesian nonparametric approach to infer multiple reward functions and adapt the MDP architecture, effectively handling uncertainties in cancer data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If frequency-based statistical methods are used to analyze cancer data, then the analysis process is simple, but the ability to infer meaningful latent reward functions and policies is insufficient
Solution Approach 1:
The patent introduces an inverse reinforcement learning system as an intermediary between observed cancer progression data and latent reward functions. This system uses experience data from cancer cell interactions with the environment to infer underlying reward structures that frequency-based methods cannot capture, thereby improving measurement precision while maintaining analytical tractability through the MDP framework
2Measurement precision
If explicit associations between experiences and reward functions are required, then the reward estimation is accurate, but the system cannot handle ambiguous and uncertain data in complex environments
Solution Approach 1:
The patent transforms the problem by changing parameters from direct reward observation to latent reward inference through inverse reinforcement learning. By modeling experiences as sequences in an MDP framework with latent policies and reward functions, the system can handle ambiguous data without requiring explicit reward associations, thus improving adaptability while maintaining estimation accuracy through gradient-based optimization
3Reliability
If multiple latent reward functions are inferred using Bayesian nonparametric approach, then the system handles uncertainty better, but the computational complexity increases
Solution Approach 1:
The patent segments the inference process into distinct iterative steps: generating MDPs, initializing partitions, updating experience assignments, and refining latent reward functions through gradient updates. This segmentation of the Bayesian nonparametric approach into manageable computational stages improves reliability through systematic exploration of reward function space while controlling computational complexity through structured iteration
4Measurement precision
If the MDP architecture is adapted using latent features from highest posterior probability reward functions, then the model accuracy improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent implements feedback loops where latent reward functions are updated based on experience assignments, and MDP architectures are refined using latent features from highest posterior probability functions. This iterative feedback mechanism improves measurement precision by continuously refining estimates while managing detection difficulty through gradient-based optimization that guides the search toward high-probability solutions
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating latent reward functions from a set of experiences each experience specifying a respective sequence of state transitions of an environment being interacted with by an agent that is controlled using a respective latent policy. In one aspect, a method includes: generating a current Markov Decision Process (MDP); initializing a current assignment which assigns the set of experiences into a first number of partitions that are each associated with a respective latent reward function; updating the current assignment, including, for each experience: selecting a partition from a second number of candidate partitions; and assigning the experience to the selected partition; and updating the latent reward functions in accordance with a specified update rule; and updating the current MDP using latent features associated with particular latent reward functions that are determined to have highest posterior probability.