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.

Papers

February 10, 2024

Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks
Annie Wong, Jacob de Nobel, Thomas Bäck, Aske Plaat, Anna V. Kononova
Deep Reinforcement Learning Evolution Strategy Reinforcement Learning Benchmark Linear Policy

February 9, 2024

Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning
Enrico Russo, Francesco Giulio Blanco, Maurizio Palesi, Giuseppe Ascia, Davide Patti, Vincenzo Catania
Reinforcement Learning Deep Reinforcement Learning DNN Framework Quality of Service Multi Accelerator Real Time Scheduling

February 8, 2024

February 7, 2024

February 5, 2024

February 4, 2024

Deep Reinforcement Learning

Papers

Solving Deep Reinforcement Learning Benchmarks with Linear Policy Networks

Towards Fair and Firm Real-Time Scheduling in DNN Multi-Tenant Multi-Accelerator Systems via Reinforcement Learning

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

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

Deep Reinforcement Learning for Picker Routing Problem in Warehousing

DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design

Deep Exploration with PAC-Bayes

Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks

INViT: A Generalizable Routing Problem Solver with Invariant Nested View Transformer