Explainable Deep Reinforcement Learning via Factorized Reward Networks
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Solution Overview
Problem
Current deep reinforcement learning systems lack the ability to provide human-understandable explanations for their actions, especially in high-dimensional visual input domains, making it difficult to interpret agent behavior and ensure that learned representations generalize like human understanding.
Innovation Solution
A method is introduced that uses a compound reward function to learn a policy through a reinforcement learning algorithm, establishing separate state-action value networks for each reward term, which produce human-understandable outputs explaining the chosen actions without compromising network convergence, by employing factorized rewards and policy gradients, allowing for efficient and interpretable learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep reinforcement learning systems are used to achieve high performance in complex domains, then learning capability and decision-making are improved, but the ability to provide human-understandable explanations deteriorates
Solution Approach 1:
The patent segments the reward function into multiple atomic reward terms, each associated with a separate state-action value network. This segmentation allows the system to maintain complex learning capabilities while providing explainable breakdowns of decision-making by attributing actions to specific reward components, resolving the contradiction between learning performance and explainability.
Solution Approach 2:
The patent introduces an intermediary explanation generation mechanism that uses the factorized state-action value networks to produce human-understandable explanations. This intermediary layer translates the internal representations of the deep reinforcement learning system into interpretable outputs, maintaining both learning capability and explainability without compromising either aspect.
2Measurement precision
If a compound reward function with multiple terms is used to capture complex objectives, then the accuracy of action selection is improved, but the complexity of the learning system increases
Solution Approach 1:
The patent segments the compound reward function into atomic reward terms, each handled by a separate state-action value network. This segmentation maintains the precision of action selection by preserving the full reward structure while reducing system complexity through modular architecture, where each network handles a specific aspect of the reward function independently.
Solution Approach 2:
The patent implements a universal explanation framework where the same factorized state-action value networks serve both as components of the learning system and as generators of human-understandable explanations. This multi-functionality reduces overall system complexity by eliminating the need for separate explanation models while maintaining action selection accuracy.
3Loss of information
If separate state-action value networks are established for each reward term to enable explanations, then explainability is improved, but computational resources and system complexity increase
Solution Approach 1:
The patent segments the explanation generation task across multiple separate state-action value networks, each dedicated to a specific reward term. This segmentation improves explainability by providing detailed breakdowns of why actions were taken relative to each reward component, while the modular architecture manages computational complexity through parallel processing of independent networks.
Solution Approach 2:
The patent applies partial action by establishing separate state-action value networks only for reward terms that require explanation, rather than creating separate networks for all possible functions. This selective approach provides sufficient explainability for the critical reward components while minimizing the increase in computational resources and system complexity.
Data Source
AI summary
A policy based on a compound reward function is learned through a reinforcement learning algorithm at a learning network. The policy is used to choose an action of a plurality of possible actions. A state-action value network is established for each of the two or more reward terms. The state-action value networks are separated from the learning network. A human-understandable output is produced to explain why the action was taken based on each of the state action value networks.