Enhancing Reinforcement Learning With Cross-Domain Knowledge Transfer via Seeded Graph Matching.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning

Background:

• Transfer reinforcement learning (TRL) enhances agent efficiency by reusing knowledge from related tasks.
• Existing cross-domain TRL methods face limitations due to strict assumptions on state-space relationships.
• Bridging disparate state and action spaces in TRL remains a significant challenge.

Purpose of the Study:

• To propose a novel, generalizable approach for cross-domain TRL.
• To enable knowledge transfer between reinforcement learning (RL) tasks with varied state and action spaces.
• To overcome limitations of prior methods relying on strong prior assumptions.

Main Methods:

• Modeling RL tasks as directed graphs.
• Utilizing seeded graph matching for aligning source and target tasks irrespective of state-action space differences.
• Developing a policy-based transfer algorithm that leverages task alignment to improve target RL performance.

Main Results:

• Demonstrated effectiveness of seeded graph matching for aligning diverse RL tasks.
• Significant performance improvements in target RL tasks through the proposed policy-based transfer algorithm.
• Validation across both discrete and continuous control tasks with varying state-action spaces.

Conclusions:

• The proposed seeded graph matching approach offers a more generalizable solution for cross-domain TRL.
• This method effectively facilitates knowledge transfer across RL tasks with heterogeneous state-action spaces.
• The approach shows strong empirical validation, advancing the field of transfer reinforcement learning.