arXiv:2601.14232v2 Announce Type: replace Abstract: Pixel-based reinforcement learning agents often fail under purely visual distribution shift even when latent dynamics and rewards are unchanged, but existing benchmarks entangle multiple sources of shift and hinder systematic analysis. We introduce KAGE-Env, a JAX-native 2D platformer that factorizes the observation process into independently controllable visual axes while keeping the underlying control problem fixed. By construction, varying a visual axis affects performance only through the induced state-conditional action distribution of a
The paper addresses a critical, long-standing challenge in pixel-based reinforcement learning concerning visual generalization, which is a significant bottleneck for deploying robust AI agents.
Improved generalization in reinforcement learning agents, especially in visual domains, is key to advancing autonomous AI systems beyond narrow, controlled environments, fostering broader and more reliable applications.
This new benchmark and methodology promise to accelerate research into visual generalization for reinforcement learning, leading to more robust and adaptable AI, particularly for agents operating in dynamic, real-world visual environments.
- · AI researchers and developers
- · Robotics
- · Logistics and automation industry
- · Developers of brittle, non-generalizing AI models
- · Sectors reliant on static, non-adaptive AI
Researchers gain clearer tools to diagnose and overcome visual distribution shift in AI models.
Reinforcement learning agents become more reliable and capable of operating effectively in previously unseen visual conditions.
More robust AI systems accelerate the development and deployment of autonomous agents capable of performing complex tasks in varied real-world settings.
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Read at arXiv cs.LG