Neural Network External Memory Augmentation via Reinforcement Learning
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Solution Overview
Problem
Current neural network systems face limitations in optimizing memory access processes, leading to suboptimal performance in interacting with external memory, particularly in selecting between content-based and location-based addressing methods.
Innovation Solution
An augmented neural network system that incorporates a memory interface subsystem using reinforcement learning to select memory access processes, allowing the neural network to generate hard decisions for memory access, thereby improving performance by effectively utilizing external memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is used to select memory access processes, then memory access performance is improved, but system complexity increases
Solution Approach 1:
The neural network output is segmented into two distinct portions: a reinforcement learning output portion for selecting memory access processes and a differentiable output portion for memory operations. This segmentation allows each portion to specialize in its function, improving overall memory access performance while managing system complexity through functional separation.
Solution Approach 2:
The memory interface subsystem acts as an intermediary between the neural network and external memory, translating neural network outputs into appropriate memory access operations. This intermediary layer manages the complexity of interacting with external memory by providing a standardized interface that handles both reinforcement learning-based selection and differentiable-based operations.
2Productivity
If hard decisions are generated for memory access selection, then memory utilization efficiency is improved, but training difficulty increases
Solution Approach 1:
The system dynamically switches between soft probabilistic outputs and hard decisions based on the specific memory access operation required. During training, the system can use soft outputs for gradient computation while generating hard decisions for actual memory access selection, allowing efficient memory utilization while maintaining trainability through dynamic adaptation of output types.
Solution Approach 2:
The neural network changes its output parameters based on the task at hand, generating reinforcement learning outputs for memory access process selection and differentiable outputs for memory operations. This parameter changing capability allows the system to produce hard decisions when needed for efficient memory access while maintaining the ability to train using gradient-based methods.
3Adaptability or versatility
If external memory is integrated with neural network, then system capability is improved, but device complexity increases
Solution Approach 1:
The memory interface subsystem provides universal functionality by handling multiple types of memory operations (read, write, erase) and supporting both reinforcement learning-based and differentiable-based access methods. This multi-functional interface allows the neural network to effectively utilize external memory for various tasks including storing and retrieving sequences, accumulating evidence, and maintaining context, thereby improving system capability while managing complexity through a unified interface.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for augmenting neural networks with an external memory using reinforcement learning. One of the methods includes providing an output derived from the system output portion of the neural network output as a system output in the sequence of system outputs; selecting a memory access process from a predetermined set of memory access processes for accessing the external memory from the reinforcement learning portion of the neural network output; writing and reading data from locations in the external memory in accordance with the selected memory access process using the differentiable portion of the neural network output; and combining the data read from the external memory with a next system input in the sequence of system inputs to generate a next neural network input in the sequence of neural network inputs.