The Advantage-based Optimization Method for Reinforcement Learning addresses the challenges of large, high-dimensional action spaces in real-world scenarios. Traditional value-based reinforcement learning algorithms often struggle with convergence difficulties, instability, and high computational complexity in such environments. A prevalent approach involves generating independent sub-actions within each dimension of the action space, but this method introduces bias, hindering the learning of optimal policies. The proposed method introduces an advantage-based optimization method and an algorithm named Advantage Branching Dueling Q-network (ABQ). ABQ incorporates a baseline mechanism to tune the action value of each dimension, leveraging the advantage relationship across different sub-actions. With this approach, the learned policy can be optimized for each dimension. Empirical results demonstrate that ABQ outperforms BDQ, achieving 3%, 171%, and 84% more cumulative rewards in HalfCheetah, Ant, and Humanoid environments, respectively. Furthermore, ABQ exhibits competitive performance when compared against two continuous action benchmark algorithms, DDPG and TD3.
Advantage Branching Dueling Q-network (ABQ)
Dueling Q-network
OpenAI Gym environments
Cumulative rewards, Convergence rate
Cloud-based, On-premises
Yes
Yes
Advantage-based optimization, High-dimensional action space handling
No
Standard GPU for deep learning
Linux, Windows, macOS
Compatible with reinforcement learning environments
None
None
None
Yes
Active community on GitHub
Research team from leading AI institutions
Varies with environment
Low
Moderate
None
None
Limited to specific environments, Requires tuning
Technology, Robotics
Robotic control, Autonomous vehicle navigation
Research institutions, Robotics companies
API, SDK
High
Community support, Documentation
None
Command-line interface
No
None
Open-source
Yes
None
None
None
1.0
Open-source software
Yes
REST API for integration
Open-source
0.00
USD
MIT License
01/12/2023
15/12/2023
+1-800-555-ABQ1
Supports multiple environments, High scalability
Yes