Risk- and Ambiguity-Sensitive Neural Control for Agent Decisions
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Solution Overview
Problem
Conventional reinforcement learning approaches result in risk-neutral and ambiguity-neutral agents, failing to account for the need for risk-aware and ambiguity-sensitive behaviors in real-world tasks where actions with uncertain outcomes can have negative consequences.
Innovation Solution
Implementing ambiguity-sensitive and risk-sensitive neural networks to control agents, using ensembles of action selection neural networks and meta-policy neural networks to generate policies that consider higher-order moments of action scores, enabling agents to make informed decisions in uncertain environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional reinforcement learning approaches are used, then the agent can complete tasks efficiently, but the agent becomes risk-neutral and ambiguity-neutral, failing to account for uncertain outcomes
Solution Approach 1:
The patent divides the action selection process into multiple independent neural networks (ensemble of action selection neural networks), each evaluating actions differently. This segmentation allows the system to separately assess risk and ambiguity dimensions while maintaining overall task efficiency through coordinated decision-making.
Solution Approach 2:
The patent introduces higher-order moments (variance, skewness) of action scores as additional decision dimensions beyond expected return. By transforming the decision space from single-dimensional expected value to multi-dimensional risk-ambiguous space, the agent can simultaneously optimize for efficiency and reliability.
2Reliability
If risk-sensitive and ambiguity-sensitive neural networks are implemented, then the agent can make informed decisions in uncertain environments, but the system complexity increases
Solution Approach 1:
The meta-policy neural network serves multiple functions: it aggregates outputs from the ensemble, evaluates risk and ambiguity, and generates final action selections. This multi-functionality reduces the need for separate dedicated components for each function, thereby managing complexity while maintaining comprehensive risk awareness.
Solution Approach 2:
The patent combines multiple action selection neural networks into an ensemble that shares common architectural components and training procedures. By merging similar functional elements and using shared parameters, the system reduces redundant complexity while maintaining the benefits of multiple perspectives on action evaluation.
3Reliability
If the agent avoids ambiguous and risky actions, then the safety and reliability improve, but the task completion efficiency may decrease
Solution Approach 1:
The patent implements dynamic action selection where the agent adjusts its risk and ambiguity tolerance based on the current state and ensemble outputs. The meta-policy network dynamically balances safety and efficiency by considering the distribution of outcomes rather than fixed risk avoidance, allowing efficient task completion while maintaining safety through adaptive decision-making.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for controlling agents. In particular, an agent can be controlled using an action selection system that is risk-sensitive, ambiguity-sensitive, or both.