Sampling Rate Decay in Hindsight Experience Replay for Robot Control.

Area of Science:

• Robotics
• Artificial Intelligence
• Machine Learning

Background:

• Deep reinforcement learning (DRL) agents face challenges in robotic control with sparse rewards and vast state spaces due to infrequent successful experiences.
• Hindsight Experience Replay (HER) and ARCHER methods utilize unsuccessful experiences as successful ones for different goals, but use a fixed sampling rate.
• Fixed sampling rates can introduce bias and do not account for the varying importance of hindsight experiences during training.

Purpose of the Study:

• To investigate the impact of a variable sampling rate for hindsight experience on DRL training performance.
• To propose a sampling rate decay strategy that reduces hindsight experiences as training progresses.

Main Methods:

• Implemented a sampling rate decay strategy for hindsight experience replay.
• Validated the proposed method on three robotic control tasks within the OpenAI Gym suite.
• Compared the performance against standard HER and ARCHER methods.

Main Results:

• The proposed variable sampling rate strategy demonstrated improved training performance on two out of three robotic control tasks.
• The method also showed increased convergence speed compared to HER and ARCHER in two tasks.
• Comparable training performance and convergence speed were observed on the third task.

Conclusions:

• A variable sampling rate for hindsight experience, specifically a decay strategy, can enhance training efficiency in DRL for robotic control.
• The proposed method offers a potential improvement over fixed-rate hindsight experience replay techniques.
• Further research could explore adaptive sampling rates based on task complexity or learning progress.