Distributional generative adversarial imitation learning with reproducing kernel generalization.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Robotics

Background:

• Generative adversarial imitation learning (GAIL) frames imitation learning (IL) as matching expert and learned policy state-action distributions.
• Generalization and computational properties of policy classes are critical for GAIL's effectiveness.
• Instability in GAIL, particularly with off-policy training, is often caused by Q-value overestimation.

Purpose of the Study:

• To enhance the generalization capabilities of Generative adversarial imitation learning (GAIL).
• To introduce distributional reinforcement learning (RL) into GAIL for improved stability and performance.
• To propose a novel algorithm, greedy distributional soft gradient (GDSG), for solving GAIL.

Main Methods:

• Proving generalization guarantees in GAIL for controlled policy classes.
• Integrating distributional RL with GAIL to address Q-value overestimation.
• Developing the greedy distributional soft gradient (GDSG) algorithm incorporating maximum entropy objectives.

Main Results:

• Demonstrated that policy generalization can be guaranteed in GAIL under controlled conditions.
• Showcased that distributional RL alleviates Q-value overestimation, enhancing GAIL's stability.
• Verified through experiments in MuJoCo environments that GDSG outperforms previous GAIL variants in mimicking expert demonstrations.

Conclusions:

• The proposed GDSG algorithm effectively improves imitation learning by leveraging distributional RL and maximum entropy objectives.
• GDSG offers enhanced performance, sample efficiency, and stability compared to existing GAIL methods.
• The study confirms the benefits of controlled policy classes and distributional RL for robust imitation learning.