Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
40039912
Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning ...
Neural networks : the official journal of the International Neural Network Society
40055886
Extrapolation reasoning in temporal knowledge graphs (TKGs) aims at predicting future facts based on historical data, and finds extensive application in diverse real-world scenarios. Existing TKG reasoning methods primarily focus on capturing the fac...
Neural networks : the official journal of the International Neural Network Society
40048754
Existing deep reinforcement learning (DRL) algorithms suffer from the problem of low sample efficiency. Episodic memory allows DRL algorithms to remember and use past experiences with high return, thereby improving sample efficiency. However, due to ...
This work presents a novel Voice in Head (ViH) framework, that integrates Large Language Models (LLMs) and the power of semantic understanding to enhance robotic navigation and interaction within complex environments. Our system strategically combine...
The safety of pedestrians in urban transportation systems has emerged as a significant research topic. As a vulnerable group within this transportation framework, pedestrians encounter heightened safety risks in complex urban road environments. Prote...
Neural networks : the official journal of the International Neural Network Society
39965524
Navigating multi-agent reinforcement learning (MARL) environments with sparse rewards is notoriously difficult, particularly in suboptimal settings where exploration can be prematurely halted. To tackle these challenges, we introduce Hierarchical Sym...
Neural networks : the official journal of the International Neural Network Society
39965523
Real-world multi-agent decision-making systems often have to satisfy some constraints, such as harmfulness, economics, etc., spurring the emergence of Constrained Multi-Agent Reinforcement Learning (CMARL). Existing studies of CMARL mainly focus on t...
Journal of the experimental analysis of behavior
40072340
Robots are increasingly used alongside Skinner boxes to train animals in operant conditioning tasks. Similarly, animals are being employed in artificial intelligence research to train various algorithms. However, both types of experiments rely on uni...
Neural networks : the official journal of the International Neural Network Society
40058179
The dependency on extensive expert knowledge for defining subgoals in hierarchical reinforcement learning (HRL) restricts the training efficiency and adaptability of HRL agents in complex, dynamic environments. Inspired by human-guided causal discove...
IEEE transactions on neural networks and learning systems
40030342
While reinforcement learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged ...