Neural Map Architecture for Long-Term Memory in Reinforcement Learning
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Solution Overview
Problem
Deep Reinforcement Learning (DRL) agents face limitations in memory architectures, particularly in partially observable environments, as they are restricted to short-term memory and require manual selection of stored information, leading to inefficiencies and redundancy in memory usage.
Innovation Solution
The neural map architecture introduces an adaptable write operator and a spatially structured memory system that learns to store arbitrary information over long time lags, using a DNC-style external memory with a two-dimensional map where each position represents a distinct memory, enabling efficient navigation in multi-dimensional environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If memory networks store past M states in a fixed memory pool, then the memory capacity is increased, but redundant information is stored and domain expert knowledge is required to set M
Solution Approach 1:
The neural map enables the system to automatically manage its own memory by learning what information is salient and worth storing, eliminating the need for external domain expertise to configure memory parameters. The write operator autonomously determines which states to store based on their importance, allowing the system to self-optimize its memory usage without manual intervention or redundant storage.
2Device complexity
If memory networks fix the memory pool size M, then the architecture is simpler, but it cannot adapt to different time horizons and requires manual configuration
Solution Approach 1:
The neural map implements dynamic memory management where the effective memory capacity adapts automatically to different time horizons and task requirements. Instead of a fixed M parameter, the system dynamically adjusts which memory locations are active and how far back in time it looks, allowing the same architecture to efficiently handle various temporal scales without manual reconfiguration.
3Use of energy by moving object
If DRL agents use simple memory architectures like LSTM or temporal convolution, then the computational cost is lower, but they are limited to short-term temporal context
Solution Approach 1:
The neural map transitions from sequential temporal processing (1D) to spatial-structured memory access (2D grid), allowing efficient access to long-term memory without proportionally increasing computational cost. The spatial organization enables parallel access patterns and optimized memory retrieval that reduce the computational burden compared to traditional sequential approaches like LSTM, while achieving unbounded temporal context.
4Duration of action of stationary object
If external neural memories use structured memory like DNC, then long-term planning is enabled, but frequent overwriting of memory locations occurs
Solution Approach 1:
The neural map applies local quality by making memory locations spatially differentiated and location-aware. Different regions of the 2D grid serve different functional purposes, and the write operator learns to target specific locations based on the nature of the information being stored. This spatial differentiation reduces random overwriting and preserves important long-term information while still allowing updates where appropriate.
Data Source
AI summary
A computer-implemented system and method for storing data associated with an agent in a multi-dimensional environment via a memory architecture. The memory architecture is structured so that each unique position in the environment corresponds to a unique position within the memory architecture, thereby allowing the memory architecture to store features located at a particular position in the environment in a memory location specific to that location. As the agent traverses the environment, the agent compares the features at the agent's particular position to a summary of the features stored throughout the memory architecture and writes the features that correspond to the summary to the coordinates in the memory architecture that correspond to the agent's position. The system and method allows agents to learn, using a reinforcement signal, how to behave when acting in an environment that requires storing information over long time steps.