- The paper introduces Gated Path Planning Networks (GPPNs) that reformulate Value Iteration Networks with LSTM-style gating to enhance training stability.
- It demonstrates that GPPNs achieve faster convergence, require fewer iterations, and exhibit superior robustness to hyperparameter variations compared to VINs.
- Empirical results confirm improved performance across 2D maze and 3D navigation tasks, validating the effectiveness of recurrent-convolutional architectures.
Gated Path Planning Networks: A New Approach to Differentiable Path Planning
The paper "Gated Path Planning Networks" presents a novel approach to improving the performance and training stability of differentiable path planning modules by reformulating the Value Iteration Networks (VINs) in terms of recurrent-convolutional networks. The authors introduce the Gated Path Planning Networks (GPPNs) that leverage gated recurrent update equations such as those utilized by Long Short-Term Memory (LSTM) networks.
Value Iteration Networks have been popular due to their ability to perform environment navigation tasks while maintaining end-to-end differentiability. However, VINs are known for their training instability, sensitivity to initialization, and susceptibility to hyperparameter variations. The primary innovation of this research lies in reframing VINs to incorporate standard gated recurrent operators, aiming to mitigate these optimization issues.
The paper empirically validates that GPPNs outperform VINs on multiple dimensions including learning speed, hyperparameter robustness, number of iterations, and generalization capacity. These advantages hold across different environments, ranging from 2D mazes with varying transition dynamics and sizes to 3D settings such as ViZDoom, where planners work from first-person RGB inputs rather than top-down views.
The methods utilize convolutional networks to predict a map design from RGB images and employ LSTM recurrent updates to propagate spatial knowledge effectively within these environments. This allows GPPNs to handle larger kernel sizes and reduce the number of iterations required for path planning, thus demonstrating superior performance on complex tasks compared to VINs.
Experimental results demonstrate that GPPNs are less sensitive to random seeds and hyperparameters, reflecting the utility of the gating mechanisms in stabilizing training. Furthermore, they exhibit less variance in performance across different initialization conditions and converge faster than VINs. These empirical insights are corroborated quantitatively by strong performance metrics, including a higher percentage of optimal paths generated.
In the broader context of reinforcement learning and planning, this work suggests that the traditional assumptions and inductive biases in differentiable path planning modules may not be essential, and that more generalized RNN-like architectures with gating mechanisms can yield superior outcomes. Future research in this domain can explore extending GPPNs to even more complex environments and integrating them with other reinforcement learning frameworks to potentially enhance their applicability to real-world navigation and autonomous systems. The relaxation of architectural biases, as presented in this work, motivates further developments in differentiable planning with improved robustness and scalability considerations.