Tsallis Entropy Regularization for Linearly Solvable MDP and Linear Quadratic Regulator Analysis

Kernekoncepter

The author explores the use of Tsallis entropy for regularization in linearly solvable MDP and linear quadratic regulators, aiming to balance exploration and sparsity in control policies.

Resumé

The content delves into the application of Tsallis entropy as a one-parameter extension of Shannon entropy for optimal control. It discusses how this approach can achieve high entropy while maintaining sparsity in control policies through numerical examples and theoretical derivations. The study formulates Tsallis entropy regularized optimal control problems, deriving Bellman equations and investigating linearly solvable Markov decision processes and linear quadratic regulators. The analysis showcases the utility of Tsallis entropy regularization in achieving a balance between exploration and sparsity in control laws.

Key points include:

• Introduction of Tsallis entropy as a regularization method.
• Application to linearly solvable MDPs and linear quadratic regulators.
• Derivation of Bellman equations for optimal control policies.
• Numerical examples demonstrating high entropy with maintained sparsity.
• Discussion on the implications for real-world applications like robotics.

Statistik

In [4], Tsallis entropy is used to regularize optimal transport problems to obtain high-entropy but sparse solutions.
For q = 0.25, V∗(T) is given by 22.040, 23.040, 25.284, 25.336.
For q = 0.25, C(T)(1) is calculated as 22.991.

Citater

"The objective is to balance traditional cost minimization with maximization of deformed q-entropy."
"Optimal control policies achieve high entropy while maintaining sparsity."

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Tsallis Entropy Regularization for Linearly Solvable MDP and Linear Quadratic Regulator

by Yota Hashizu... kl. arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01805.pdf

Dybere Forespørgsler

How does the bounded support from q-Gaussian distributions impact real-world applications

The bounded support from q-Gaussian distributions has significant implications for real-world applications, particularly in systems where constraints on the state or control inputs are crucial. In scenarios like robotics or autonomous vehicles, operating within a specific range is essential for safety and stability. The bounded support ensures that the system remains within predefined limits, preventing it from entering unsafe regions or making erratic movements. This feature enhances the robustness of the system by constraining its behavior to known and safe areas.
Additionally, in fields such as finance or economics, where certain variables must remain within specified boundaries (e.g., stock prices), utilizing distributions with bounded support can help model and predict outcomes more accurately. By incorporating q-Gaussian distributions with limited support into these models, researchers can better capture the inherent constraints present in these systems and make more reliable forecasts.

What are the practical implications of balancing exploration and sparsity in transportation planning

Balancing exploration and sparsity in transportation planning offers several practical implications for optimizing logistics operations efficiently. Exploration allows for flexibility in adapting to changing conditions such as traffic congestion or unforeseen events during transit routes. By promoting exploration through entropy regularization techniques like Tsallis entropy, transportation planners can ensure that their systems remain adaptable and responsive to dynamic environments.
On the other hand, enforcing sparsity helps streamline operations by reducing unnecessary complexity and resource allocation. Sparse control policies focus resources on critical tasks while minimizing redundant actions or routes. In transportation planning specifically, maintaining sparsity ensures efficient use of available resources like vehicles or drivers while still providing robust solutions to handle disruptions effectively.
By striking a balance between exploration and sparsity in transportation planning using methods like Tsallis entropy regularization, practitioners can achieve optimized routing strategies that are both flexible enough to adapt to changes yet streamlined enough to operate efficiently under normal conditions.

How might the limitations of applying Sinkhorn iteration due to non-additivity affect future research directions

The limitations of applying Sinkhorn iteration due to non-additivity pose interesting challenges for future research directions in optimization problems involving Tsallis entropy regularizations. Since traditional algorithms like Sinkhorn heavily rely on additive properties of Shannon entropy which do not hold true for Tsallis entropy due to its non-additive nature; new approaches need development tailored explicitly towards handling this unique characteristic.
One potential direction could involve exploring alternative iterative methods specifically designed for non-additive entropies such as Tsallis entropy regularization terms. Researchers may need to devise novel optimization algorithms capable of accommodating the complexities introduced by non-additivity while ensuring convergence and efficiency comparable to existing techniques used with Shannon entropy-based formulations.
Furthermore, investigating mathematical frameworks that embrace non-additive entropies more naturally could open up avenues for developing specialized solvers tailored towards solving optimization problems under Tsallis entropy regularizations effectively. By addressing these challenges head-on through innovative algorithmic developments and theoretical advancements focused on non-additive properties; researchers can pave the way towards unlocking new possibilities in optimal control theory across various domains.