Neural Finite State Machine for Interpretable Robotic Control
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Solution Overview
Problem
Deep reinforcement learning techniques face challenges in mission-critical robotic applications due to difficulties in simulating realistic environments and the 'black-box' nature of deep neural networks, leading to uncertainty in handling unusual situations and poor policy transfer.
Innovation Solution
A neural finite state machine (NFSM) is introduced, which combines the decision-making capabilities of deep RL with the structured constraints of finite state machines, using state-dependent neural networks to determine transitions and actions, allowing for weight-sharing and gating to improve processing efficiency and interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep reinforcement learning techniques are used for decision-making, then sophisticated sequential decision-making capability is improved, but interpretability and reliability in mission-critical applications deteriorate due to the black-box nature of deep neural networks
Solution Approach 1:
The patent segments the decision-making process into discrete states and transitions, replacing the monolithic deep neural network with a structured finite state machine architecture. Each state has associated policy networks that operate independently, making the overall system more interpretable while maintaining decision-making sophistication through the segmented structure.
Solution Approach 2:
The patent introduces an intermediary layer between the environment and the deep neural networks in the form of a finite state machine. This intermediary structure mediates the decision-making process by constraining transitions to predefined states, thereby improving interpretability and reliability while still leveraging the power of deep learning for policy determination within each state.
2Measurement precision
If realistic environment simulation is attempted for training, then training accuracy is improved, but system complexity and computational resources required deteriorate
Solution Approach 1:
The patent segments the complex environment simulation into simplified state representations and transition models. By dividing the environment into discrete states with defined transition probabilities, the system achieves adequate training accuracy without requiring full realistic simulation complexity, thus reducing computational burden while maintaining essential training fidelity.
3Reliability
If state-dependent neural networks are used to control transitions, then interpretability and generalization are improved, but processing efficiency may deteriorate due to multiple policy networks
Solution Approach 1:
The patent implements weight-sharing across multiple policy networks, where certain layers or parameters are shared among different state-dependent networks. This universality allows the system to maintain interpretability through state-dependent control while improving processing efficiency by reducing redundant computations across the multiple policy networks.
Data Source
AI summary
A system includes a state-dependent action policy and a state-dependent transition policy. The state-dependent action policy determines an action based on environment states and a current agent state selected from a predetermined group of agent states. The state-dependent transition policy is implemented using one or more machine learning models and is configured to control transitions between agent states from the predetermined group of agent states.