Curriculum learning with Hindsight Experience Replay for sequential object manipulation tasks

Area of Science:

• Artificial Intelligence
• Robotics
• Machine Learning

Background:

• Learning complex tasks from scratch is a significant challenge for both humans and artificial agents.
• Curriculum learning offers a solution by breaking down complex tasks into a sequence of simpler, progressively difficult source tasks.
• Hindsight Experience Replay (HER) is a technique used to improve learning in environments with sparse rewards.

Purpose of the Study:

• To develop and evaluate a novel algorithm that integrates curriculum learning with Hindsight Experience Replay (HER).
• To enable artificial agents to learn sequential object manipulation tasks efficiently, even with multiple goals and sparse feedback.
• To demonstrate the algorithm's effectiveness in a simulated environment without task-specific adjustments.

Main Methods:

• The proposed algorithm combines curriculum learning principles with Hindsight Experience Replay (HER).
• It leverages the inherent recurrent structure of object manipulation tasks.
• The learning process is implemented entirely within the original simulation environment, avoiding modifications for each source task.

Main Results:

• The algorithm demonstrated significant improvements in learning sequential object manipulation tasks compared to vanilla HER.
• Performance was evaluated on three challenging throwing tasks in a simulated environment.
• The approach effectively handles multiple goals and sparse feedback scenarios.

Conclusions:

• The integration of curriculum learning and HER provides a powerful framework for tackling complex sequential manipulation tasks.
• The algorithm's ability to learn in the original simulation without task adjustments enhances its generalizability.
• This method offers a promising direction for advancing reinforcement learning in robotics and AI.