Core Concepts
Integrating the Hard-Thresholding operator with Natural Evolution Strategies (NES) to mitigate the impact of task-irrelevant features in reinforcement learning problems.
Abstract
The content discusses a novel approach called NESHT that combines the Natural Evolution Strategies (NES) algorithm with the Hard-Thresholding (HT) operator to address the challenge of task-irrelevant features in reinforcement learning problems.
Key highlights:
- NES is a competitive alternative for model-free reinforcement learning, but it assumes all input features are task-relevant, which can lead to poor performance in real-world problems with irrelevant features.
- The authors propose NESHT, which integrates the HT operator into the NES framework to promote sparsity and ensure only pertinent features are utilized.
- The authors provide a comprehensive analysis to establish the convergence of NESHT, resolving the lingering uncertainty regarding the compatibility of natural gradients with the hard-thresholding operator.
- Extensive empirical evaluations on Mujoco and Atari environments demonstrate the effectiveness of NESHT in mitigating the impact of irrelevant features and outperforming other reinforcement learning and evolution strategy algorithms.
Stats
The fitness function F(θ) is bounded, i.e., there exists a constant B > 0 such that |F(θ)| ≤ B for all θ.
The variance of the cumulative reward fτ(θ) is bounded by a constant C > 0 for all θ in the optimization trajectory.
Quotes
"Evolution Strategies (ES) have emerged as a competitive alternative for model-free reinforcement learning, showcasing exemplary performance in tasks like Mujoco and Atari."
"Yet, an inherent assumption in ES—that all input features are task-relevant—poses challenges, especially when confronted with irrelevant features common in real-world problems."