arXiv:2509.26000v3 Announce Type: replace Abstract: Asymmetric reinforcement learning leverages privileged information available during training to improve learning under partial observability. Existing asymmetric actor-critic methods typically assume access to the full environment state to condition the critic during training, which is often unrealistic in practice. We introduce the informed asymmetric actor-critic framework that allows the critic to be conditioned on arbitrary state-dependent privileged signals, and show that any such signal yields unbiased policy gradient estimates. This su
This research emerges as AI systems become more complex and require increasingly efficient yet robust training methods under real-world, partially observable conditions.
Improving reinforcement learning efficiency and robustness with privileged information, even when full state access is impractical, accelerates the development of more capable and deployable AI agents.
The ability to condition critics on arbitrary state-dependent privileged signals, not just full state access, expands the applicability and practicality of asymmetric reinforcement learning techniques.
- · AI researchers
- · Robotics companies
- · Autonomous systems developers
More sophisticated and robust AI agents can be developed and deployed in environments where complete state information is unavailable.
This could lead to faster progress in complex real-world AI applications like autonomous driving, advanced robotics, and intelligent control systems.
The reduced reliance on full state observability might lower the data collection burden and computational costs for certain AI training paradigms, democratizing access to advanced RL techniques.
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Read at arXiv cs.LG