Hierarchical adaptive value estimation for multi-modal visual reinforcement learning
Integrating RGB frames with alternative modality inputs is gaining increasing traction in
many vision-based reinforcement learning (RL) applications. Existing multi-modal vision …
many vision-based reinforcement learning (RL) applications. Existing multi-modal vision …
Parameterized Decision-making with Multi-modal Perception for Autonomous Driving
Autonomous driving is an emerging technology that has advanced rapidly over the last
decade. Modern transportation is expected to benefit greatly from a wise decision-making …
decade. Modern transportation is expected to benefit greatly from a wise decision-making …
Towards learning-based energy-efficient online coordinated virtual network embedding framework
Z Duan, T Wang - Computer Networks, 2024 - Elsevier
Network virtualization is a highly effective technology for resource sharing within data
centers, enabling the coexistence of multiple heterogeneous virtual networks in a shared …
centers, enabling the coexistence of multiple heterogeneous virtual networks in a shared …
Parameterized Decision-Making with Multi-Modality Perception for Autonomous Driving
Autonomous driving is an emerging technology that has advanced rapidly over the last
decade. Modern transportation is expected to benefit greatly from a wise decision-making …
decade. Modern transportation is expected to benefit greatly from a wise decision-making …
TorchDriveEnv: A Reinforcement Learning Benchmark for Autonomous Driving with Reactive, Realistic, and Diverse Non-Playable Characters
The training, testing, and deployment, of autonomous vehicles requires realistic and efficient
simulators. Moreover, because of the high variability between different problems presented …
simulators. Moreover, because of the high variability between different problems presented …
Generative AI for Deep Reinforcement Learning: Framework, Analysis, and Use Cases
As a form of artificial intelligence (AI) technology based on interactive learning, deep
reinforcement learning (DRL) has been widely applied across various fields and has …
reinforcement learning (DRL) has been widely applied across various fields and has …