AIMC Topic: Reinforcement, Psychology

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The design of consumer behavior prediction and optimization model by integrating DQN and LSTM.

PloS one
Amidst the rapid evolution of e-commerce and the growing abundance of consumer shopping data, accurately identifying consumer interests and enabling targeted outreach has become a critical focus for merchants and researchers. This study introduces th...

Integrated decision-control for social robot autonomous navigation considering nonlinear dynamics model.

PloS one
Reinforcement learning (RL) has demonstrated significant potential in social robot autonomous navigation, yet existing research lacks in-depth discussion on the feasibility of navigation strategies. Therefore, this paper proposes an Integrated Decisi...

Encoding flexible gait strategies in stick insects through data-driven inverse reinforcement learning.

Bioinspiration & biomimetics
Stick insects exhibit remarkable adaptive walking capabilities across diverse environments; however, the mechanisms underlying their gait transitions remain poorly understood. Although reinforcement learning (RL) has been employed to generate insect-...

Egocentric value maps of the near-body environment.

Nature neuroscience
Body-part-centered response fields are pervasive in single neurons, functional magnetic resonance imaging, electroencephalography and behavior, but there is no unifying formal explanation of their origins and role. In the present study, we used reinf...

Multi-agent self-attention reinforcement learning for multi-USV hunting target.

Neural networks : the official journal of the International Neural Network Society
A reinforcement learning (RL) method based on the multi-head self-attention (MSA) mechanism is proposed to solve the challenge of multiple unmanned surface vehicles (multi-USV) hunting target at the surface. The kinematic, dynamic, and environmental ...

Reinforcement Learning-Driven Path Generation for Ankle Rehabilitation Robot Using Musculoskeletal-Informed Energy Optimization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
In rehabilitation robotics, optimizing energy consumption and high interaction forces is essential to prevent unnecessary muscle fatigue and excessive joint loading as they often cause an inefficient trajectory planning and disrupt natural movement p...

A Survey on Causal Reinforcement Learning.

IEEE transactions on neural networks and learning systems
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 ...

Mastering diverse control tasks through world models.

Nature
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to w...

Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Annals of the New York Academy of Sciences
Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment....

Critical scenarios adversarial generation method for intelligent vehicles testing based on hierarchical reinforcement architecture.

Accident; analysis and prevention
The widespread deployment of intelligent vehicles necessitates comprehensive testing across diverse driving scenarios. A significant challenge is generating critical testing scenarios to accurately evaluate vehicle performance. To overcome the limita...