CHDP: Cooperative Hybrid Diffusion Policies for Reinforcement Learning in Parameterized Action Space
arXiv:2601.05675v2 Announce Type: replace Abstract: Hybrid action space, which combines discrete choices and continuous parameters, is prevalent in domains such as robot control and game AI. However, efficiently modeling and optimizing hybrid discrete-continuous action space remains a fundamental challenge, mainly due to limited policy expressiveness and poor scalability in high-dimensional settings. To address this challenge, we view the hybrid action space problem as a fully cooperative game and propose a \textbf{Cooperative Hybrid Diffusion Policies (CHDP)} framework to solve it. CHDP emplo
The development of more sophisticated AI models and hardware necessitates improved control mechanisms for complex environments like robotics and gaming, where hybrid action spaces are common.
Efficiently modeling and optimizing hybrid discrete-continuous action spaces is a fundamental challenge for advancing AI applications in real-world scenarios, directly impacting autonomy and agent performance.
This framework offers a new approach to policy expressiveness and scalability in high-dimensional hybrid action spaces, potentially leading to more capable and adaptable AI agents.
- · AI researchers
- · Robotics companies
- · Game AI developers
- · Automation sector
- · Developers reliant on less scalable RL frameworks
- · Industries that cannot adapt to new autonomous agent capabilities
Improved performance of AI agents in environments requiring both discrete decisions and continuous parameter tuning.
Accelerated development and deployment of autonomous systems in complex physical and digital worlds.
Enhanced AI capabilities could enable new forms of automation and interaction, reshaping labor markets and human-machine collaboration.
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Read at arXiv cs.AI