Generalized Early Stopping in Evolutionary Direct Policy Search

Conceitos essenciais

Proposing a generic early stopping method for direct policy search that significantly reduces computation time without the need for problem-specific knowledge.

Resumo

The content discusses the proposal of a generalized early stopping method for direct policy search to save computation time. It addresses the issue of lengthy evaluation times in optimization problems, especially in tasks like direct policy search. The proposed method is tested in various environments and compared with problem-specific stopping criteria, showing significant time savings and comparable performance.

Abstract:

• Lengthy evaluation times are common in optimization problems.
• Proposed early stopping method aims to save computation time.
• Tested in various environments with promising results.

Introduction:

• Evolutionary algorithms increasingly used in applications like games and robotics.
• Direct policy search algorithms require many evaluations, leading to long learning times.
• Surrogate models can be used to replace costly objective functions with faster alternatives.

Related Work:

• Many direct policy search tasks use problem-specific early stopping methods.
• Various approaches have been proposed for hyperparameter optimization.
• Early stopping based on the objective function alone may not always be applicable.

Generalized Early Stopping for Direct Policy Search (GESP):

• GESP is designed for problems with incremental approximation capabilities.
• Resuming an evaluation is not possible once stopped by GESP.
• Assumption of approximation quality ensures proper identification of solutions.

Experimentation:

• Validation of GESP through experiments in different direct policy search tasks.
• Results show significant reduction in computation time with GESP implementation.
• Comparison with problem-specific stopping criteria reveals effectiveness of GESP.

Estatísticas

Often when evaluating solution over a fixed time period it becomes clear that the objective value will not increase with additional computation time (for example when a two wheeled robot continuously spins on the spot).
We test the introduced stopping criterion in five direct policy search environments drawn from games, robotics and classic control domains, and show that it can save up to 75% of the computation time.

Citações

"Lengthy evaluation times are common in many optimization problems such as direct policy search tasks."
"The proposed method only looks at the objective value at each time step and requires no problem specific knowledge."

Principais Insights Extraídos De

Generalized Early Stopping in Evolutionary Direct Policy Search

by Etor Arza,Le... às arxiv.org 03-22-2024

https://arxiv.org/pdf/2308.03574.pdf

Perguntas Mais Profundas

How can generic early stopping methods be adapted for specific problem domains

Generic early stopping methods can be adapted for specific problem domains by incorporating domain-specific knowledge or criteria into the stopping mechanism. This adaptation can involve modifying the conditions under which evaluations are stopped to align with the unique characteristics of the problem at hand. For example, in a robotics application where a robot's movement is critical, additional criteria related to movement patterns or sensor data could be integrated into the early stopping decision process. By customizing the early stopping rules based on domain expertise, generic methods can be tailored to optimize performance and efficiency within specific problem domains.

What are the limitations of using monotone increasing objective functions for early stopping

Using monotone increasing objective functions for early stopping has limitations when applied to scenarios where objectives do not strictly follow this pattern. In such cases:

Inaccurate Assessment: Early stopping based on monotone increasing functions may prematurely terminate evaluations that have potential for improvement but exhibit temporary dips in performance.
Suboptimal Solutions: The focus on only increasing values might overlook solutions that fluctuate before reaching optimal states.
Misleading Results: Stopping solely based on upward trends could lead to suboptimal outcomes if true peaks occur after initial declines.
These limitations highlight the need for adaptive approaches like GESP (Generalized Early Stopping) that consider broader dynamics of objective functions beyond strict monotonous trends.

How does GESP compare to other existing methods for reducing computational burden

GESP offers several advantages compared to other existing methods for reducing computational burden:

Generality: GESP does not require problem-specific knowledge and adapts well across various optimization tasks without extensive customization.
Efficiency: By leveraging incremental approximations and grace periods, GESP optimizes evaluation time effectively while maintaining solution quality.
Flexibility: It complements existing task-specific stoppage criteria rather than replacing them entirely, providing an adaptable approach suitable for diverse applications.
Overall, GESP stands out as a versatile and efficient method capable of significantly reducing computation time in direct policy search environments while ensuring comparable performance levels.