Accelerating Policy Optimization through Extremum-Seeking Action Selection

Core Concepts

Extremum-Seeking Action Selection (ESA) improves the quality of exploratory actions in policy optimization, reducing the sampling of low-value trajectories and accelerating learning.

Abstract

The paper proposes the Extremum-Seeking Action Selection (ESA) method to improve both exploration and exploitation in sampling actions for policy optimization in continuous spaces. ESA follows the strategies of Extremum-Seeking Control (ESC) by applying sinusoidal perturbations on the sampled actions in each step to obtain actions of higher action values and also improve exploration.
The key insights are:

ESC methods can be particularly sample efficient for locally optimizing unknown objectives, compared to policy gradient methods.
The scale of ESA perturbations on the sampled actions needs to be carefully chosen to balance the trade-off between fast local improvement with ESC and reliable policy improvement over all states.
The ability of tracking dynamic objectives makes ESC methods particularly suitable for handling problems in the continuous domain by shifting the focus from states to improving entire trajectories over time.
The authors demonstrate that adding ESA to standard policy optimization algorithms like PPO and SAC can clearly improve the learning performance in various continuous control problems.

Stats

The paper does not provide any specific numerical data or statistics. It focuses on describing the proposed ESA method and providing high-level comparisons with baseline approaches.

Quotes

The paper does not contain any direct quotes that are particularly striking or support the key logics.

Key Insights Distilled From

Extremum-Seeking Action Selection for Accelerating Policy Optimization

by Ya-Chien Cha... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01598.pdf

Deeper Inquiries

How can the ESA method be extended to handle constraints or safety requirements in the action space?

The ESA method can be extended to handle constraints or safety requirements in the action space by incorporating these constraints into the perturbation and filtering process. When sampling actions from the stochastic policies, the perturbations applied can be adjusted based on the known constraints to ensure that the sampled actions remain within the feasible action space. This can be achieved by modifying the amplitude and frequency of the perturbations to guide the exploration towards safe and feasible actions. Additionally, the high-pass filtering step can be tailored to prioritize regions of the action space that satisfy the constraints, thereby improving the quality of the sampled actions while respecting safety requirements.

How does the performance of ESA compare to other exploration techniques that leverage model-based information, such as uncertainty-aware exploration?

The performance of ESA compared to other exploration techniques that leverage model-based information, such as uncertainty-aware exploration, can be evaluated based on several factors. ESA, being a model-free approach, excels in scenarios where the underlying dynamics of the system are complex or unknown. It relies on real-time optimization through extremum-seeking control, which can adapt to the system's response without requiring explicit models. In contrast, uncertainty-aware exploration techniques often rely on probabilistic models to estimate uncertainty and guide exploration. While uncertainty-aware methods can provide valuable insights into the system's dynamics, they may suffer from model inaccuracies or computational complexity.
ESA's strength lies in its ability to dynamically improve the quality of sampled actions without explicit knowledge of the system dynamics, making it suitable for challenging control problems where model-based approaches may struggle. However, in scenarios where accurate models are available and uncertainty estimation is crucial for exploration, uncertainty-aware techniques may outperform ESA by leveraging probabilistic models to make informed decisions.

Can the frequency-domain analysis techniques used in ESC be further leveraged to provide theoretical guarantees on the convergence and stability of the overall policy optimization process?

The frequency-domain analysis techniques used in Extremum-Seeking Control (ESC) can indeed be further leveraged to provide theoretical guarantees on the convergence and stability of the overall policy optimization process. By analyzing the system's response in the frequency domain, ESC can track local optima and adapt the control inputs to converge towards these optima without requiring explicit knowledge of the system dynamics. This frequency-domain analysis enables ESC to exploit the second-order properties of the objective function and dynamically adjust the estimation variables to approach the optimum.
To provide theoretical guarantees on the convergence and stability of the policy optimization process, the frequency-domain analysis in ESC can be used to derive stability conditions and convergence rates. By analyzing the frequency response of the system to perturbations, one can establish conditions under which the estimation variables converge to local optima. This analysis can lead to the development of stability proofs and convergence theorems that ensure the effectiveness of the ESA method in improving policy optimization efficiency. Leveraging frequency-domain techniques can provide a rigorous theoretical foundation for understanding the behavior of ESA in the context of reinforcement learning for control tasks.