Neural Map: Structured Memory for Deep Reinforcement Learning (1702.08360v1)

Abstract: A critical component to enabling intelligent reasoning in partially observable environments is memory. Despite this importance, Deep Reinforcement Learning (DRL) agents have so far used relatively simple memory architectures, with the main methods to overcome partial observability being either a temporal convolution over the past k frames or an LSTM layer. More recent work (Oh et al., 2016) has went beyond these architectures by using memory networks which can allow more sophisticated addressing schemes over the past k frames. But even these architectures are unsatisfactory due to the reason that they are limited to only remembering information from the last k frames. In this paper, we develop a memory system with an adaptable write operator that is customized to the sorts of 3D environments that DRL agents typically interact with. This architecture, called the Neural Map, uses a spatially structured 2D memory image to learn to store arbitrary information about the environment over long time lags. We demonstrate empirically that the Neural Map surpasses previous DRL memories on a set of challenging 2D and 3D maze environments and show that it is capable of generalizing to environments that were not seen during training.

• The paper introduces Neural Map, a structured memory system that overcomes limitations of traditional DRL architectures in handling long-term dependencies.
• It details a GRU-based update mechanism alongside global, context, and local read/write operations to boost training stability and efficiency.
• Empirical evaluations show superior navigation and zero-shot generalization in 2D and 3D tasks compared to methods like LSTM and MemNN policies.

Neural Map: Structured Memory for Deep Reinforcement Learning

The paper "Neural Map: Structured Memory for Deep Reinforcement Learning" presents an innovative approach to overcoming the limitations associated with memory systems in deep reinforcement learning (DRL) agents, particularly when these agents operate in partially observable environments. Prior methodologies primarily relied on temporal convolution techniques or LSTM layers to address partial observability; however, these approaches have inherent limitations, notably their dependence on a fixed, recent history of $k$ frames.

This research introduces the Neural Map, a memory system designed to efficiently handle the challenges of 3D environments, which are typical settings for DRL agents. The Neural Map utilizes a spatially structured 2D memory component that facilitates storing and accessing environmental information over extended time horizons without restricting the memory to a time-fixed sequence of past frames. The structured memory system is coupled with a customized, adaptable write operation that enables efficient memory usage while mitigating redundancy—a common issue encountered in memory networks.

Empirical evaluations demonstrate the Neural Map's superior performance over existing memory architectures, such as LSTM-based and memory network (MemNN) policies, across various task suites involving both 2D and 3D maze challenges. These tasks test the agent's capability to retain and utilize information over time effectively. The results suggest that the Neural Map's structured memory approach enhances the generalization capabilities of DRL agents, enabling successful navigation in previously unseen environments.

The Neural Map's architecture features distinct operations such as a global read operation for summarizing the entire map, a context read operation that allows associative storage and retrieval, and a local write operation that contributes to efficient information storage at the agent's specific location within the map. The architecture's design presents significant advancements over typical recurrent networks and write-based memory structures used in contemporary models like the Differentiable Neural Computer (DNC).

A key innovation of this work is the GRU-based update mechanism for the Neural Map, which enhances training stability and learning efficiency. The GRU update mechanism outperforms the standard update in terms of both learning speed and accuracy, becoming a critical component in the Neural Map's performance. Furthermore, the paper discusses extensions to this structured memory model, proposing adaptations such as ego-centric mapping, which could eliminate the dependency on absolute agent positioning within the map.

The successful integration of Neural Map in complex environments such as those generated using the ViZDoom platform underscores its potential in 3D navigation contexts—extending the capabilities of DRL agents significantly. The tests conducted not only reinforce the model's robustness within training configurations but also exhibit a high degree of zero-shot generalization to novel scenarios, a testament to its refined memory management and navigation faculties.

The implications of introducing a structured spatial memory in DRL are manifold. Practically, this framework could catalyze the development of more autonomous systems capable of performing complex tasks over longer durations without explicit prior knowledge of environmental dynamics. Theoretically, the Neural Map contributes to the understanding and advancement of memory representation techniques within neural architectures—potentially inspiring future research directions that explore hybrid models combining the strengths of structured memories with other AI paradigms.

In conclusion, the Neural Map represents a substantial forward stride in addressing the persistent challenge of memory management in DRL, particularly in spatially complex settings. It significantly extends the utility and applicability of DRL agents in real-world tasks that demand comprehensive environmental understanding and long-term memory utilization. As such, further exploration and refinement of this model could lead to an increased efficacy of DRL in diverse application domains.