Highway Value Iteration Networks (2406.03485v1)

Abstract: Value iteration networks (VINs) enable end-to-end learning for planning tasks by employing a differentiable "planning module" that approximates the value iteration algorithm. However, long-term planning remains a challenge because training very deep VINs is difficult. To address this problem, we embed highway value iteration -- a recent algorithm designed to facilitate long-term credit assignment -- into the structure of VINs. This improvement augments the "planning module" of the VIN with three additional components: 1) an "aggregate gate," which constructs skip connections to improve information flow across many layers; 2) an "exploration module," crafted to increase the diversity of information and gradient flow in spatial dimensions; 3) a "filter gate" designed to ensure safe exploration. The resulting novel highway VIN can be trained effectively with hundreds of layers using standard backpropagation. In long-term planning tasks requiring hundreds of planning steps, deep highway VINs outperform both traditional VINs and several advanced, very deep NNs.

• The paper introduces highway VINs that improve long-term planning through innovative skip connections and training mechanisms.
• It integrates aggregate, exploration, and filter gates to address challenges in training very deep networks for dynamic decision-making.
• Empirical results demonstrate highway VINs achieve up to 98.61% success in tasks with over 100 planning steps, outperforming conventional VINs.

Highway Value Iteration Networks for Improved Long-term Planning

The paper introduces an innovation in neural network architectures with the development of Highway Value Iteration Networks (highway VINs). This architecture seeks to address the challenges associated with long-term planning in value iteration networks (VINs). Traditional VINs have demonstrated effectiveness in a variety of applications, such as path planning and dynamic decision-making. However, their utility diminishes significantly in tasks requiring extensive planning due to difficulties in training very deep networks.

Theoretical Foundation and Methodology

The proposed highway VIN architecture integrates principles from highway networks and highway reinforcement learning to improve the representational capacity and training efficiency of VINs. The architecture introduces several key components:

1. Aggregate Gate: This component constructs skip connections that facilitate efficient information flow across multiple network layers, thereby aiding in long-term credit assignment.
2. Exploration Module: Designed to enhance information and gradient diversity during training, this module introduces controlled stochastic exploration, which widens the breadth of potential solutions and improves robustness.
3. Filter Gate: Ensures safe and efficient exploration by discarding non-contributory pathways, thus preserving the convergent properties of the network.

Numerical Results

Through comprehensive experiments, the paper establishes that highway VINs can be effectively trained with hundreds of layers, outperforming conventional VINs and other deep learning architectures in long-term planning tasks. For instance, highway VINs achieve a success rate of up to 98.61% in scenarios requiring over 100 planning steps, significantly surpassing the capabilities of both VINs and gated path planning networks (GPPNs). The architecture's robustness is further demonstrated in both 2D and 3D navigation tasks, showcasing its applicability in real-world scenarios.

Implications and Future Perspectives

The deployment of highway VINs has significant implications for reinforcement learning, particularly in areas necessitating deep planning and extended horizon tasks. The architecture's adeptness at handling long-term dependencies is promising for complex decision-making scenarios, including autonomous navigation and robotic control.

Looking forward, future research could explore the integration of multiple policies within the exploration module to further enhance planning efficiency and robustness. Moreover, scaling the architecture to tackle larger and more complex tasks will be crucial in maintaining its applicability across diverse applications in AI. The combination of deep planning capabilities and efficient training mechanisms positions highway VINs as a powerful tool in advancing long-term strategic decision-making within machine learning frameworks.