Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Continual reinforcement learning (CRL) agents struggle to adapt to dynamic environments without catastrophic forgetting.
• Existing methods lack efficient mechanisms for promptly adapting to environmental changes.

Purpose of the Study:

• To propose DaCoRL (dynamics-adaptive continual RL), a novel framework for adaptive CRL in dynamic environments.
• To address the challenge of balancing adaptation to new tasks and retention of previously learned information.

Main Methods:

• DaCoRL employs progressive contextualization to cluster tasks into contexts using online Bayesian infinite Gaussian mixture clustering.
• An expandable multihead neural network approximates the context-conditioned policy, expanding synchronously with new contexts.
• Knowledge distillation regularization is used to mitigate catastrophic forgetting.

Main Results:

• DaCoRL demonstrates consistent superiority over existing methods in stability, overall performance, and generalization.
• Experiments on robot navigation and MuJoCo locomotion tasks validate the framework's effectiveness.
• The method accurately classifies current tasks into existing contexts or creates new ones without prior environmental change indicators.

Conclusions:

• DaCoRL provides a robust and generalizable framework for dynamics-adaptive continual reinforcement learning.
• The proposed approach effectively handles dynamic environments, enhancing agent adaptability and knowledge retention.
• DaCoRL represents a significant advancement in continual reinforcement learning for real-world applications.