Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Robotics

Background:

• Reinforcement learning (RL) aims to optimize agent control policies for maximum future rewards.
• Model-free RL learns policies directly from data, often requiring extensive samples.
• Model-based RL estimates environment dynamics, potentially improving data efficiency.

Purpose of the Study:

• To develop a novel model-based reinforcement learning method.
• To enhance policy learning efficiency using limited data.
• To demonstrate the practical utility of the proposed approach.

Main Methods:

• Combines policy gradients (a model-free method) with parameter-based exploration.
• Utilizes least-squares conditional density estimation for accurate transition model learning.
• Integrates model estimation and policy learning within a unified framework.

Main Results:

• The proposed model-based RL method shows practical usefulness in experiments.
• Achieves effective policy learning with reduced data requirements compared to model-free approaches.
• Demonstrates the synergy between advanced transition model estimation and policy search.

Conclusions:

• The novel model-based RL approach provides a data-efficient alternative for learning optimal control policies.
• Combining policy gradients with accurate transition model estimation is a promising direction for RL research.
• The method is practically useful and offers advantages in scenarios with expensive data collection.