Efficient sample reuse in policy gradients with parameter-based exploration.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Policy gradient methods are crucial for reinforcement learning, especially in continuous action spaces like robot control.
• A key challenge is mitigating the high variance in policy gradient estimates, which hinders reliable policy updates.

Purpose of the Study:

• To develop a highly effective policy gradient method with reduced variance for improved reinforcement learning performance.
• To address the challenge of reliable policy updates in continuous action space problems.

Main Methods:

• The proposed method integrates three key techniques: policy gradients with parameter-based exploration, importance sampling for data reuse, and an optimal baseline for variance reduction.
• Theoretical analysis of gradient estimate variance was conducted.

Main Results:

• The combined approach significantly reduces the variance of policy gradient estimates.
• Extensive experiments demonstrate the method's effectiveness and usefulness in practical applications.

Conclusions:

• The novel policy gradient method offers a robust solution for reinforcement learning problems with continuous actions.
• This approach enhances the reliability of policy updates, paving the way for more stable robot control and similar applications.