Reinforcement Learning Agent Feature Contribution Analysis
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Solution Overview
Problem
Reinforcement learning agents employing Deep Neural Networks (DNNs) face challenges in interpretability, leading to a time-consuming and difficult process of manually tuning reward functions, as the black-box nature of DNNs makes it hard to understand their behavior and adjust the reward function effectively.
Innovation Solution
The method involves determining the relative contribution of each feature to the action determination by the model, allowing for a dynamic reward adjustment based on feature importance, thereby improving the training efficiency and robustness of reinforcement learning agents by rewarding correct decision-making and penalizing incorrect feature-based actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a Deep Neural Network model is used to determine actions in reinforcement learning, then the agent can handle complex environments and make sophisticated decisions, but the interpretability of the model decreases making it difficult to understand its behavior
Solution Approach 1:
The patent introduces an intermediary module that analyzes the DNN model's internal operations and generates explanations for its decisions. This mediator translates the black-box DNN outputs into interpretable information about which features influenced the action selection, thereby resolving the contradiction between maintaining sophisticated decision-making capability and providing interpretability.
Solution Approach 2:
The system implements feedback by using the interpretability information generated from the DNN model to improve the reward function. The explanations about feature contributions are fed back into the reinforcement learning process, allowing the system to refine its reward structure based on understanding of the model's actual behavior, thus maintaining both complexity and interpretability.
2Reliability
If manual observation and tuning of the reward function is performed to ensure correct modeling, then the interpretability and correctness can be improved, but the process becomes time-consuming and requires continuous human intervention
Solution Approach 1:
The patent enables the system to self-adjust the reward function by automatically analyzing the DNN model's feature contributions and generating appropriate reward structures without human intervention. The system serves itself by using its own interpretability outputs to refine its reward function, eliminating the need for continuous manual tuning while maintaining reliability.
Solution Approach 2:
The system performs preliminary analysis of the DNN model's behavior and pre-adjusts the reward function before deployment or before issues arise. By proactively configuring the reward function based on initial interpretability analysis, the system avoids time-consuming iterative manual tuning later in the process.
3Reliability
If the agent performs exhaustive trial and error exploration in highly-varying conditions, then it can discover optimal actions, but the training time becomes excessively long
Solution Approach 1:
The patent applies preliminary configuration of the reward function based on DNN feature contribution analysis before the exhaustive trial-and-error process begins. This pre-configuration guides the exploration by establishing a more informed reward structure, allowing the agent to discover optimal actions more efficiently without sacrificing the thoroughness needed for highly-varying conditions.
Data Source
AI summary
A computer implemented method for configuring a reinforcement learning agent to perform an efficient reinforcement learning procedure, wherein the reinforcement learning agent comprises a model trained using a machine learning process to determine actions to be performed by the reinforcement learning agent. The method comprises using the model to determine an action to perform, based on values of a set of features obtained in an environment; determining, for a first feature in the set of features, a first indication of a relative contribution of the first feature, compared to other features in the set of features, to the determination of the action by the model; and determining a reward to be given to the reinforcement learning agent in response to performing the action, based on the first feature and the first indication.