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

May 9, 2023

Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning
Runqing Wang, Gang Wang, Jian Sun, Fang Deng, Jie Chen
Reinforcement Learning Deep Reinforcement Learning Flexible Job Shop Scheduling Problem

May 7, 2023

Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity
Jinghao Xin, Jinwoo Kim, Zhi Li, Ning Li
Deep Reinforcement Learning Robot Simulation Reinforcement Learning Benchmark Local Path Planning

May 5, 2023

May 2, 2023

More Than an Arm: Using a Manipulator as a Tail for Enhanced Stability in Legged Locomotion
Huang Huang, Antonio Loquercio, Ashish Kumar, Neerja Thakkar, Ken Goldberg, Jitendra Malik
Deep Reinforcement Learning Robot Person Legged Robot Human Like Locomotion Behavior Legged Locomotion Best Arm Serial Manipulator Thin Tail

May 1, 2023

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Remi Munos, Will Dabney, Diana L Borsa
Deep Reinforcement Learning Representation Learning Meaningful Representation Environment Exploration Singular Value Decomposition Deep Reinforcement Learning Agent Exploration Problem Predictive State Representation

April 30, 2023

April 28, 2023

April 27, 2023

April 26, 2023

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
Tuomas Haarnoja, Ben Moran, Guy Lever, Sandy H. Huang, Dhruva Tirumala, Jan Humplik, Markus Wulfmeier, Saran Tunyasuvunakool, Noah Y. Siegel, Roland Hafner, Michael Bloesch, Kristian Hartikainen, Arunkumar Byravan, Leonard Hasenclever, Yuval Tassa, Fereshteh Sadeghi, Nathan Batchelor, Federico Casarini, Stefano Saliceti, Charles Game, Neil Sreendra, Kushal Patel, Marlon Gwira, Andrea Huber, Nicole Hurley, Francesco Nori, Raia Hadsell, Nicolas Heess
Deep Reinforcement Learning Zero Shot Humanoid Robot Bipedal Robot Deep RL

April 25, 2023

April 24, 2023

Deep Reinforcement Learning

Papers

Flexible Job Shop Scheduling via Dual Attention Network Based Reinforcement Learning

Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity

Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators

Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization

More Than an Arm: Using a Manipulator as a Tail for Enhanced Stability in Legged Locomotion

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition

Posterior Sampling for Deep Reinforcement Learning

A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings

Optimal Scheduling in IoT-Driven Smart Isolated Microgrids Based on Deep Reinforcement Learning

Learning adaptive manipulation of objects with revolute joint: A case study on varied cabinet doors opening

Adversarial Policy Optimization in Deep Reinforcement Learning

BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading

SocNavGym: A Reinforcement Learning Gym for Social Navigation

Discovering Object-Centric Generalized Value Functions From Pixels

Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning

Dynamic Datasets and Market Environments for Financial Reinforcement Learning

A optimization framework for herbal prescription planning based on deep reinforcement learning

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks

Efficient Halftoning via Deep Reinforcement Learning

When to Replan? An Adaptive Replanning Strategy for Autonomous Navigation using Deep Reinforcement Learning