Reinforcement Learning Control With Knowledge Shaping.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Traditional reinforcement learning (RL) often requires extensive data for new tasks.
• Leveraging prior knowledge can significantly accelerate learning and improve performance.
• Existing transfer learning methods in RL are largely empirical.

Purpose of the Study:

• To develop a transfer reinforcement learning framework that effectively utilizes prior knowledge.
• To formalize knowledge transfer within the value function using a novel approach called reinforcement learning with knowledge shaping (RL-KS).
• To provide theoretical analysis and empirical validation for the proposed RL-KS method.

Main Methods:

• Formalizing knowledge transfer by incorporating prior knowledge into the value function.
• Developing two principled methods for representing prior knowledge within the RL-KS framework.
• Conducting extensive evaluations on benchmark RL problems and a real-world robotic control task.

Main Results:

• Demonstrated simulation verifications of the RL-KS method.
• Provided analysis of algorithm convergence and solution optimality.
• Achieved new theoretical results on positive knowledge transfer, distinct from policy-invariant methods.

Conclusions:

• The proposed RL-KS framework offers a principled and theoretically grounded approach to transfer learning in RL.
• RL-KS enables effective utilization of prior knowledge, enhancing learning efficiency and performance.
• The method shows promise in complex, real-time control applications, such as human-in-the-loop robotics.