Continual Diffuser (CoD): Mastering Continual Offline RL With Experience Rehearsal.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Static datasets are common in artificial neural networks for gaming and QA.
• Real-world applications like reinforcement learning (RL) involve dynamic, sequential tasks.
• Adapting to changing tasks while retaining knowledge presents a plasticity-stability tradeoff challenge.

Purpose of the Study:

• To propose a novel rehearsal-based continual diffusion model, Continual Diffuser (CoD).
• To enhance agent capabilities for quick adaptation (plasticity) and knowledge retention (stability) in sequential tasks.
• To evaluate CoD's performance against existing methods on a diverse benchmark.

Main Methods:

• Developed an offline benchmark with 90 tasks across multiple domains.
• Trained the Continual Diffuser (CoD) model using sequential modeling and conditional generation.
• Implemented a rehearsal buffer by preserving and replaying a subset of previous datasets.

Main Results:

• CoD demonstrated a favorable plasticity-stability tradeoff.
• The model outperformed existing diffusion-based methods and other baselines on most tasks.
• Experiments confirmed CoD's effectiveness in sequential task learning.

Conclusions:

• The proposed Continual Diffuser (CoD) effectively balances plasticity and stability in continual learning scenarios.
• CoD offers a promising solution for RL agents operating in dynamic environments.
• The method shows superior performance compared to current diffusion models and baselines.