Highway Value Iteration Networks

Highway Value Iteration Networks (Highway VINs) enhance the long-term planning capabilities of Value Iteration Networks (VINs) by integrating a Highway Value Iteration algorithm, which includes an aggregate gate, an exploration module, and a filter gate, enabling the effective training of very deep networks and outperforming traditional methods in complex planning tasks.

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.

Method

The authors used a methodology that involved integrating the Highway Value Iteration algorithm into the Value Iteration Networks (VINs) to create Highway VINs. This integration included the introduction of three key components: the aggregate gate, the exploration module, and the filter gate. The aggregate gate was designed to improve information flow across layers, the exploration module aimed to increase the diversity of information and gradient flow in spatial dimensions, and the filter gate was implemented to ensure safe exploration by filtering out unproductive paths. The authors also conducted experiments to validate the effectiveness of Highway VINs, particularly in 2D and 3D maze navigation tasks, and provided a computational complexity analysis to demonstrate the efficiency of their approach.

Main Finding

The authors discovered that by embedding the Highway Value Iteration algorithm into the structure of Value Iteration Networks (VINs), they could significantly improve the networks' ability to handle long-term planning tasks. This integration resulted in the creation of Highway VINs, which were capable of being trained effectively with hundreds of layers using standard backpropagation. The authors found that Highway VINs outperformed traditional VINs and several advanced deep neural networks in long-term planning tasks, particularly in maze navigation problems with long path lengths. They also observed that the success rate of Highway VINs remained high even as the complexity of the tasks increased, which was not the case for the other models tested.

Conclusion

The conclusion of the paper is that Highway Value Iteration Networks (Highway VINs) represent a substantial advancement in the field of neural networks for planning tasks. By successfully integrating the Highway Value Iteration algorithm into the VIN architecture, the authors have created a network that can be trained to a much greater depth than traditional VINs, which in turn allows for superior performance in complex, long-term planning scenarios. The paper concludes that Highway VINs are a promising direction for future research and have the potential to address more significant and challenging planning problems.

Keywords

Highway Value Iteration Networks, Highway VINs, Value Iteration Networks, VINs, planning module, aggregate gate, exploration module, filter gate, long-term planning, neural networks, reinforcement learning, maze navigation, computational complexity, backpropagation, success rate, shortest path length, convergence, optimal value function, skip connections, embedded policies, stochasticity, deep learning, machine learning, planning tasks, neural architecture, inductive bias, credit assignment, policy performance, value function, action-value function.