Deep RL

Deep reinforcement learning (Deep RL) aims to train agents to make optimal decisions in complex environments by learning from experience, primarily through trial and error. Current research emphasizes improving sample efficiency, addressing challenges like value overestimation and hyperparameter sensitivity, and enhancing the interpretability and robustness of learned policies across diverse domains. Prominent algorithms include actor-critic methods (e.g., A2C, PPO, SAC, TD3, DDPG), and research explores architectures like Mixture-of-Experts networks and the integration of symbolic reasoning and program synthesis for improved generalization and long-horizon task solving. These advancements hold significant potential for applications in robotics, finance, autonomous driving, and other fields requiring adaptive decision-making in dynamic environments.

Papers

January 26, 2023

Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots
Linda Dotto de Moraes, Victor Augusto Kich, Alisson Henrique Kolling, Jair Augusto Bottega, Raul Steinmetz, Emerson Cassiano da Silva, Ricardo Bedin Grando, Anselmo Rafael Cuckla, Daniel Fernando Tello Gamarra
Deep Reinforcement Learning Deep Q Network Deep RL Double Deep Q Network Terrestrial Mobile Robot

December 29, 2022

A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management
MohammadAmin Fazli, Mahdi Lashkari, Hamed Taherkhani, Jafar Habibi
Reinforcement Learning Deep Reinforcement Learning Deep RL Portfolio Management Portfolio Optimization Problem Expert Aggregation

December 15, 2022

Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Shivakanth Sujit, Pedro H. M. Braga, Jorg Bornschein, Samira Ebrahimi Kahou
Reinforcement Learning Offline Reinforcement Learning Deep RL Action Free Offline

November 20, 2022

Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
Andrew C. Li, Zizhao Chen, Pashootan Vaezipoor, Toryn Q. Klassen, Rodrigo Toro Icarte, Sheila A. McIlraith
Reinforcement Learning Case Study Policy Learning Deep RL Reward Machine Symbolic Language

October 26, 2022

Knowledge-Guided Exploration in Deep Reinforcement Learning
Sahisnu Mazumder, Bing Liu, Shuai Wang, Yingxuan Zhu, Xiaotian Yin, Lifeng Liu, Jian Li
Deep Reinforcement Learning Environment Exploration Deep RL Action Knowledge

October 23, 2022

LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation
Shuo Cheng, Danfei Xu
Motion Planning Skill Learning Long Horizon Task Deep RL Cognitive Abstraction Long Horizon Manipulation Action Abstraction Sport League

October 16, 2022

Entropy Regularized Reinforcement Learning with Cascading Networks
Riccardo Della Vecchia, Alena Shilova, Philippe Preux, Riad Akrour
Reinforcement Learning Deep Reinforcement Learning Network Programming Neural Model Deep RL Entropy Regularized

September 19, 2022

Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems
Alexander Ororbia, Ankur Mali
Reinforcement Learning Robot Control Predictive Coding Deep RL Reward Signal Spatial Prediction Sparse Reward Continuous Control

September 13, 2022

September 1, 2022

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization
Tiantian Zhang, Zichuan Lin, Yuxing Wang, Deheng Ye, Qiang Fu, Wei Yang, Xueqian Wang, Bin Liang, Bo Yuan, Xiu Li
Dynamic Environment Deep RL Continual Reinforcement Learning Conditioned Policy

August 9, 2022

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2
Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, Ajay Divakaran
Application Proficiency Deep RL Generative Replay Deep Reinforcement Learning Agent Lifelong Reinforcement Learning StarCraft II

August 1, 2022

Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling
Hou Shengren, Edgar Mauricio Salazar, Pedro P. Vergara, Peter Palensky
Deep Reinforcement Learning Deep RL Performance Comparison Renewable Generation

July 17, 2022

Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients
Thesath Nanayakkara
Reinforcement Learning Deep RL Intensive Care Deep Survival Optimal Treatment Reinforcement Learning Objective

June 6, 2022

Efficient entity-based reinforcement learning
Vince Jankovics, Michael Garcia Ortiz, Eduardo Alonso
High Efficiency Structured Representation Deep RL Fair Representation Structured Information Learning Environment

March 15, 2022

Zipfian environments for Reinforcement Learning
Stephanie C. Y. Chan, Andrew K. Lampinen, Pierre H. Richemond, Felix Hill
Reinforcement Learning Deep Reinforcement Learning Deep RL General Environment

February 5, 2022

Adversarially Trained Actor Critic for Offline Reinforcement Learning
Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal
Offline Reinforcement Learning Deep RL Stackelberg Game Public Policy Offline RL Algorithm

December 30, 2021

Self Reward Design with Fine-grained Interpretability
Erico Tjoa, Guan Cuntai
Deep Reinforcement Learning Reward Function Deep RL Interpretable by Design Inverse Reward Design

December 27, 2021

Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles
Ricardo Bedin Grando, Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling, Paulo Lilles Jorge Drews-Jr
Deep Reinforcement Learning Unmanned Aerial Vehicle Deep RL Deep Deterministic Policy Gradient Mapless Navigation

December 13, 2021

PantheonRL: A MARL Library for Dynamic Training Interactions
Bidipta Sarkar, Aditi Talati, Andy Shih, Dorsa Sadigh
Deep RL Multi Agent Setting MARL Algorithm Dynamic Training

Deep RL

Papers

Double Deep Reinforcement Learning Techniques for Low Dimensional Sensing Mapless Navigation of Terrestrial Mobile Robots

A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management

Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies

Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines

Knowledge-Guided Exploration in Deep Reinforcement Learning

LEAGUE: Guided Skill Learning and Abstraction for Long-Horizon Manipulation

Entropy Regularized Reinforcement Learning with Cascading Networks

Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems

Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Deep Reinforcement Learning Through Environmental Generalization

Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2

Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling

Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients

Efficient entity-based reinforcement learning

Zipfian environments for Reinforcement Learning

Adversarially Trained Actor Critic for Offline Reinforcement Learning

Self Reward Design with Fine-grained Interpretability

Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles

PantheonRL: A MARL Library for Dynamic Training Interactions