Scaling Learning based Policy Optimization for Temporal Tasks via Dropout

Concetti Chiave

Model-based approach for training feedback controllers in highly nonlinear environments using DT-STL and dropout to handle long-horizon temporal tasks efficiently.

Sintesi

The content introduces a model-based approach for training feedback controllers in nonlinear environments using DT-STL. It addresses challenges with long-horizon tasks by proposing a novel gradient approximation algorithm based on dropout. The methodology aims to improve training efficiency and scalability for complex spatio-temporal tasks.

Abstract:

• Introduces model-based approach for training feedback controllers.
• Uses DT-STL to handle specific task objectives and safety constraints.
• Proposes a novel gradient approximation algorithm based on dropout.
• Demonstrates efficacy on motion planning applications with complex tasks.

Introduction:

• Neural networks used for feedback control in nonlinear environments.
• Challenges with optimizing cost functions for system behavior.
• Importance of spatio-temporal task objectives expressed in DT-STL.

Training Neural Network Control Policies:

• Utilizes recurrent neural networks for control synthesis.
• Challenges with vanishing and exploding gradients in long-horizon tasks.
• Introduces sampling-based gradient approximation inspired by dropout.

Extension to Long Horizon Temporal Tasks & Higher Dimensional Systems:

• Addresses challenges with critical predicates in control synthesis.
• Proposes safe re-smoothing technique to handle non-differentiable local maxima.

Computing the Sampled Gradient:

• Differentiates between original and sampled gradients for efficient computation.
• Illustrates the methodology through examples of trajectory sampling.

Statistiche

None

Citazioni

"In each iteration, we pick some recurrent units to be 'frozen', effectively approximating the gradient propagation."
"Our key idea is to approximate the gradient during back-propagation by an approximation scheme similar to drop-out layers."

Approfondimenti chiave tratti da

Scaling Learning based Policy Optimization for Temporal Tasks via Dropout

by Navid Hashem... alle arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15826.pdf

Domande più approfondite

How can the proposed methodology be applied to real-world autonomous systems

The proposed methodology can be applied to real-world autonomous systems by leveraging the benefits of dropout-inspired techniques in control synthesis. By using sampling-based gradient approximation, critical predicates, and safe re-smoothing strategies, the approach offers a way to train neural network controllers for complex tasks in highly nonlinear environments. This methodology allows for training feedback controllers that ensure specific task objectives and safety constraints expressed in discrete-time Signal Temporal Logic (DT-STL).
In real-world applications, such as autonomous vehicles or robotic systems, this approach can be utilized to train controllers that navigate through dynamic and challenging environments while adhering to specified task requirements. For instance, in an autonomous driving scenario, the system could learn how to safely maneuver through traffic while obeying traffic rules and reaching designated destinations within certain time frames. The scalability of the method enables it to handle long-horizon temporal tasks efficiently.
By incorporating smooth semantics for DT-STL robustness computation and utilizing sampled gradients inspired by dropout techniques, the methodology provides a robust framework for training neural network controllers in real-world autonomous systems. This ensures reliable performance even in complex spatio-temporal scenarios requiring sequential decision-making over extended periods.

What are potential drawbacks or limitations of using dropout-inspired techniques in control synthesis

While dropout-inspired techniques offer significant advantages in control synthesis methodologies like the one proposed here, there are potential drawbacks and limitations that need to be considered:

Loss of Information: Dropout involves randomly deactivating neurons during training which may lead to loss of important information from the model.

Increased Training Time: Sampling-based gradient approximation requires multiple iterations with different sets of dropped-out units leading to increased computational complexity and longer training times.

Complexity Management: Managing the trade-off between accuracy and computational efficiency when selecting critical predicates or determining sampled trajectories can be challenging.

Non-Deterministic Behavior: The randomness introduced by dropout may result in non-deterministic behavior during training which could affect reproducibility.

Hyperparameter Sensitivity: Dropout methods often require careful tuning of hyperparameters such as dropout rate which adds an additional layer of complexity.

It is essential to carefully address these limitations when implementing dropout-inspired techniques for control synthesis applications.

How might the concept of critical predicates impact the scalability of the proposed approach

The concept of critical predicates can impact the scalability of the proposed approach due to its sensitivity towards changes in controller parameters affecting trajectory states associated with different critical predicates at various time steps along a trajectory.

In scenarios where there is frequent shifting between critical predicates due to small variations in controller parameters or trajectory dynamics, maintaining consistency becomes challenging.
The need for accurate detection of non-differentiable local maxima poses difficulties since incorrect gradients generated from changing critical predicates might hinder progress towards improving robustness values.
Implementing safe re-smoothing strategies helps mitigate some issues related to shifts between critical predicates but introduces complexities regarding objective function selection based on whether differentiable segments align with specific predicate evaluations or not.
Overall, managing transitions between critical predicates effectively is crucial for ensuring stability and effectiveness when scaling up this approach across diverse temporal tasks requiring precise control synthesis methodologies over extended horizons."