Improving policy exploitation in online reinforcement learning with instant retrospect action

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Online reinforcement learning (RL) algorithms face challenges with slow policy exploitation due to inefficient exploration and delayed updates.
• Existing methods struggle to achieve rapid adaptation and effective learning in dynamic environments.

Purpose of the Study:

• To introduce a novel algorithm, Instant Retrospect Action (IRA), designed to overcome the limitations of current value-based online RL methods.
• To enhance learning efficiency and final performance in continuous control tasks.

Main Methods:

• Proposing Q-Representation Discrepancy Evolution (RDE) for discriminative Q-network representations.
• Implementing Greedy Action Guidance (GAG) through action backtracking for policy constraints.
• Introducing the Instant Policy Update (IPU) mechanism to increase update frequency.
• Leveraging k-nearest-neighbor action value estimates for accurate policy learning.

Main Results:

• IRA significantly improves learning efficiency and final performance across eight MuJoCo continuous control tasks.
• The algorithm demonstrates enhanced policy exploitation and faster adaptation.
• Early-stage training conservatism in IRA helps mitigate overestimation bias in value-based RL.

Conclusions:

• IRA offers a substantial advancement in online reinforcement learning, particularly for continuous control problems.
• The proposed methods (RDE, GAG, IPU) collectively contribute to more effective and efficient RL agent training.
• IRA presents a promising direction for developing more robust and high-performing RL systems.