Reinforcement learning via conservative agent for environments with random delays.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Real-world reinforcement learning (RL) often involves delayed environmental feedback.
• Standard state representations fail under delayed feedback, hindering Markovian dynamics.
• Existing methods struggle with random delays due to their unpredictability.

Purpose of the Study:

• Propose a robust agent for decision-making under bounded random delays.
• Enable extension of constant-delay RL methods to random-delay environments.
• Develop an agent requiring minimal prior knowledge of delay distributions.

Main Methods:

• Introduced a 'conservative agent' that reformulates random-delay problems into constant-delay surrogates.
• The agent requires only the maximum delay, not the full distribution.
• Theoretical analysis and empirical evaluation on continuous control tasks.

Main Results:

• The conservative agent significantly outperforms existing baselines.
• Demonstrated superior asymptotic performance and sample efficiency.
• Performance remains invariant to delay distribution changes if maximum delay is constant.

Conclusions:

• The conservative agent provides a robust solution for RL with random feedback delays.
• It offers a generalizable framework adaptable to various constant-delay RL algorithms.
• The approach enhances learning and control in complex, real-world scenarios.