Fast reinforcement learning with generalized policy updates.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Deep Learning

Background:

• Deep reinforcement learning (DRL) shows promise for complex sequential decision-making.
• A major challenge in DRL is the substantial amount of data required for training.
• Current DRL methods struggle with problems that are computationally intractable.

Purpose of the Study:

• To address the data inefficiency in deep reinforcement learning.
• To propose a novel divide-and-conquer approach for complex decision-making problems.
• To enhance the learning speed and reduce data requirements of DRL systems.

Main Methods:

• Decomposing complex decision problems into a sequence or parallel set of simpler tasks.
• Associating each sub-task with a specific reward function within the reinforcement learning framework.
• Generalizing policy improvement and policy evaluation operations to leverage solutions from previously solved tasks.

Main Results:

• The proposed method significantly reduces the data needed to solve reinforcement learning problems.
• Task decomposition allows for leveraging solutions of prior tasks to accelerate learning on new tasks.
• When reward functions are linearly related, the problem simplifies to linear regression, further reducing data needs.

Conclusions:

• A divide-and-conquer strategy effectively mitigates the data requirements of deep reinforcement learning.
• Generalizing core RL operations enables efficient transfer of knowledge between tasks.
• This approach makes complex sequential decision-making problems more tractable and data-efficient.