arXiv:2605.30749v1 Announce Type: new Abstract: Maximum entropy reinforcement learning (MaxEnt-RL) enables robust exploration, yet practical implementations often restrict policies to simple Gaussians. While recent approaches incorporate expressive generative policies via importance-weighted supervised learning, they are prone to importance weight collapse, which limits their scalability in high-dimensional action spaces. Our key insight is to mitigate this limitation by localizing the sampling region, avoiding the weight degeneracy induced by importance sampling over the entire action space.
The continuous drive to improve reinforcement learning robustness and scalability for complex, high-dimensional tasks necessitates new approaches like FLAG to overcome current limitations.
Advanced MaxEnt-RL techniques like FLAG could unlock more robust and efficient learning for AI, particularly in applications requiring sophisticated exploration of high-dimensional environments like robotics.
This research introduces a method to mitigate importance weight collapse in MaxEnt-RL, potentially expanding the practical applicability of more expressive and complex generative policies in AI systems.
- · AI researchers
- · Robotics companies
- · Autonomous systems developers
- · Companies relying on less efficient RL methods
Improved performance and stability in reinforcement learning algorithms for complex tasks.
Faster development and deployment of sophisticated AI agents and robotic systems.
Enhanced autonomy and adaptability of AI in real-world scenarios, accelerating the adoption of agentic systems.
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Read at arXiv cs.LG