arXiv:2605.30656v1 Announce Type: new Abstract: In many practical reinforcement learning environments, observations are far higher-dimensional than the variables that matter for control. In this work, we ask: can we learn representations that capture only control-relevant features of the environment? We study this question through the empowerment objective, which maximizes an agent's influence over the environment and is widely used for unsupervised skill learning. We show that empowerment agents induce two distinct representations -- forward and backward -- that capture complementary aspects
The accelerating development in reinforcement learning and the pursuit of more efficient and generalizable AI systems make representation learning a critical current research focus.
This research outlines a method for AI agents to automatically learn control-relevant features, which could significantly improve the efficiency and applicability of AI in complex, high-dimensional environments, leading to more autonomous and capable systems.
AI systems could become much more adept at distilling essential information from vast data streams, allowing them to operate more effectively in real-world scenarios without extensive human feature engineering.
- · AI research labs
- · Robotics companies
- · Autonomous systems developers
- · Deep learning practitioners
- · Developers reliant on manual feature engineering
- · Systems with high data dimensionality friction
More efficient and generalizable reinforcement learning models are developed for various applications.
Advanced AI agents capable of higher autonomy emerge, influencing industries from manufacturing to logistics and potentially defence.
The reduced need for human supervision in complex control tasks accelerates the development and deployment of truly autonomous AI systems, impacting labor markets and societal structures.
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Read at arXiv cs.LG