OnDeFog: Online Decision Transformer under Frame Dropping
Researchers have introduced OnDeFog, an advancement in reinforcement learning designed to handle frame dropping, a common issue in real-world applications due to communication delays or sensor failures. This new method integrates the frame-dropping mitigation techniques of DeFog with the online learning capabilities of an online decision transformer (ODT). Experimental results show that OnDeFog surpasses ODT in environments with high frame-dropping rates and outperforms DeFog when dealing with datasets containing substantial amounts of low-reward data. AI
IMPACT Improves reinforcement learning agent performance in scenarios with unreliable data transmission.