Основные понятия
The core message of this paper is to introduce an adaptive step-size method for policy gradient in reinforcement learning, inspired by the Polyak step-size concept, which eliminates the need for sensitive step-size tuning and demonstrates faster convergence and more stable policies compared to existing approaches.
Аннотация
The paper addresses the challenge of sensitive step-size tuning in reinforcement learning (RL) algorithms, particularly the policy gradient method. The authors propose an adaptive step-size approach inspired by the Polyak step-size concept, which automatically adjusts the step-size without requiring prior knowledge.
Key highlights:
Adoption of the Polyak step-size idea: The authors integrate the Polyak step-size concept into the policy gradient framework, eliminating the need for sensitive step-size fine-tuning.
Investigation and resolution of issues: The authors systematically investigate and address the challenges associated with applying the Polyak step-size to policy gradient, ensuring its practicality and effectiveness.
Demonstrated performance: Through experiments on various Gym environments, the authors provide empirical evidence that their proposed method outperforms alternative approaches, showcasing faster convergence and more stable policy outcomes.
The paper first introduces the policy gradient algorithm and the Polyak step-size concept. It then discusses the issues that arise when directly applying the Polyak step-size to policy gradient, such as the stochastic update issue and the need to estimate the optimal objective function value (V*). To address these challenges, the authors propose:
Incorporating an entropy penalty to mitigate the stochastic update issue.
Employing a twin-model method to estimate V* in a more conservative and robust manner.
The authors then present their algorithm, which combines the twin-model method and the entropy penalty, and evaluate its performance on Acrobot, CartPole, and LunarLander environments. The results demonstrate that the proposed Polyak step-size approach outperforms the widely used Adam optimizer in terms of faster convergence and more stable policy outcomes.
Статистика
The paper does not provide specific numerical data or metrics to support the key logics. The performance comparisons are presented through line plots showing the reward curves.
Цитаты
The paper does not contain any striking quotes supporting the key logics.