arXiv:2606.26574v1 Announce Type: new Abstract: Many real-world control problems involve hybrid discrete-continuous action spaces. For example, steering and signaling in autonomous driving, and aiming and firing in robotics or video-games. Despite real-world hybrid factorization and reinforcement learning framework support for complex action spaces (e.g., Gymnasium, PettingZoo, TorchRL, SeedRL, Mujoco, etc), the default environments within those frameworks often implement uniform action space configurations (LunarLander, Walker2D, Cheetah, SMAC, SUMO, Ant, Atari). Landmark hybrid-action benchm
The paper addresses a critical gap in reinforcement learning environments, as hybrid action spaces are increasingly relevant for real-world robotic and autonomous systems that are rapidly developing.
Improving the handling of complex, hybrid action spaces will accelerate the development and deployment of more capable and versatile AI systems, particularly in robotics and autonomous control.
Current RL frameworks will be better equipped to model and solve real-world problems with both discrete and continuous control elements, moving beyond simplified uniform action spaces.
- · AI/ML researchers
- · Robotics industry
- · Autonomous vehicle developers
- · Video game AI
- · Developers relying solely on uniform action space benchmarks
- · Systems limited by current RL action space constraints
New benchmarks and algorithms optimized for hybrid action spaces will emerge.
More sophisticated and human-like AI agents will be possible across various domains.
Accelerated commercialization of advanced robotic and autonomous systems due to improved control capabilities.
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Read at arXiv cs.LG