Deep Reinforcement Learning
Deep reinforcement learning (DRL) aims to train agents to make optimal decisions in complex environments by learning through trial and error. Current research focuses on improving DRL's robustness, sample efficiency, and interpretability, often employing architectures like Proximal Policy Optimization (PPO), deep Q-networks (DQNs), and graph neural networks (GNNs) to address challenges in diverse applications such as robotics, game playing, and resource management. The resulting advancements have significant implications for various fields, enabling the development of more adaptable and efficient autonomous systems across numerous domains.
1498papers
Papers - Page 31
February 13, 2024
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Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning
Scaling Artificial Intelligence for Digital Wargaming in Support of Decision-Making
Intelligent Mode-switching Framework for Teleoperation
Decision Theory-Guided Deep Reinforcement Learning for Fast Learning
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching
FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning
February 7, 2024
Analyzing Adversarial Inputs in Deep Reinforcement Learning
A computational approach to visual ecology with deep reinforcement learning
Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation
Exploration Without Maps via Zero-Shot Out-of-Distribution Deep Reinforcement Learning
Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning
Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Driving
A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks