The paper discusses the challenges faced by autonomous robots when they encounter perception failures due to harsh environments or algorithm misinterpretations. The authors propose a model where such failures are…
Continue readingThis academic paper enhances previous research on exfiltration path discovery using reinforcement learning (RL). In contrast to earlier approaches that focused solely on identifying optimal paths, this work incorporates protocol…
Continue readingThe study proposes a novel action sequence predictor called Decision ConvFormer (DC), which is based on the MetaFormer architecture. DC uses local convolution filtering as the token mixer and effectively…
Continue readingHyperPPO is a reinforcement learning algorithm that uses graph hypernetworks to estimate the weights of multiple neural architectures at once. It is designed to find smaller, more efficient neural network…
Continue readingThe article discusses the Raij u framework, an innovative approach that uses reinforcement learning to automate post-exploitation in network systems. The authors argue that understanding the behaviours of attackers after…
Continue readingThe researchers at Xiamen University propose a Sequential Action induced invariant Representation (SAR) method to accurately learn task-relevant state representations from high-dimensional observations with visual distractions. This is a challenging…
Continue readingThe article discusses Onlineensembling Network (OneNet), a new approach to time series forecasting models to address the concept drift problem. OneNet updates two models focusing on time dimension dependency and…
Continue readingThis paper presents a method for improving model-based reinforcement learning (MBRL) algorithms by addressing issues related to model shift and model bias. The authors propose an optimization objective that unifies…
Continue readingThe article discusses the challenges of using reinforcement learning (RL) in real-world tasks due to dynamics gaps between real and simulated environments. It introduces a new algorithm, H2O+, which aims…
Continue readingThe article discusses a study aimed at improving the generalization of imitation learning agents in game AI through the use of data augmentation. The authors note that while imitation learning…
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