Effective Reinforcement Learning

Effective reinforcement learning (RL) aims to develop algorithms that enable agents to learn optimal behaviors through trial-and-error interaction with an environment. Current research focuses on improving RL's efficiency and robustness, particularly in complex, high-dimensional settings, by exploring novel reward shaping techniques, advanced model architectures like encoder-decoder attention mechanisms and spiking neural networks, and incorporating structural information principles to guide learning. These advancements are crucial for tackling real-world challenges in robotics, resource management (like vehicle routing), and human-computer interaction, leading to more efficient and adaptable AI systems.

Papers

June 19, 2024

Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks
Yuhao Pan, Xiucheng Wang, Nan Cheng, Qi Qiu
Reinforcement Learning Gradient Descent Spiking Neural Network Gradient Approximation Stable Learning Effective Reinforcement Learning

June 14, 2024

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning
Hao Bai, Yifei Zhou, Mert Cemri, Jiayi Pan, Alane Suhr, Sergey Levine, Aviral Kumar
Reinforcement Learning Vision Language Model Mobile Device Effective Reinforcement Learning Digital Human Autonomous Reinforcement Learning

April 15, 2024

Effective Reinforcement Learning Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Dingli Su, Angsheng Li
Reinforcement Learning Multi Agent Reinforcement Learning Structural Information Effective Reinforcement Learning Semantic Entropy Structural Entropy Path Entropy

January 31, 2024

Behind the Myth of Exploration in Policy Gradients
Adrien Bolland, Gaspard Lambrechts, Damien Ernst
Policy Gradient Optimal Policy Environment Exploration Near Optimal Policy Policy Gradient Algorithm Exploration Strategy Many Myth Effective Reinforcement Learning

January 8, 2024

Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
Joshua Levin, Randall Correll, Takanori Ide, Takafumi Suzuki, Takaho Saito, Alan Arai
Deep Reinforcement Learning Salesman Problem Effective Reinforcement Learning

July 19, 2023

Benchmarking Potential Based Rewards for Learning Humanoid Locomotion
Se Hwan Jeon, Steve Heim, Charles Khazoom, Sangbae Kim
Robotics Domain Reward Function Humanoid Robot Reward Shaping Humanoid Locomotion Effective Reinforcement Learning

July 30, 2022

Reinforcement learning with experience replay and adaptation of action dispersion
Paweł Wawrzyński, Wojciech Masarczyk, Mateusz Ostaszewski
Reinforcement Learning Adaptation Concern Black Box Optimization Experience Replay Effective Reinforcement Learning Random Exploration Action Distribution

June 27, 2022

When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
Haoyi Niu, Shubham Sharma, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming Hu, Xianyuan Zhan
Policy Evaluation Interactive Simulation Reinforcement Learning Policy Offline to Online Reinforcement Learning Effective Reinforcement Learning

June 24, 2022

Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning
Yunfei Li, Tian Gao, Jiaqi Yang, Huazhe Xu, Yi Wu
Sparse Reward Sparse Reward Environment Effective Reinforcement Learning

May 24, 2022

Concurrent Credit Assignment for Data-efficient Reinforcement Learning
Emmanuel Daucé
Action Space Data Efficient Credit Assignment Policy Actor Critic Variational Optimization Effective Reinforcement Learning Occupancy Model

April 25, 2022

Task-Induced Representation Learning
Jun Yamada, Karl Pertsch, Anisha Gunjal, Joseph J. Lim
Representation Learning Effective Reinforcement Learning Task Induced Representation

March 1, 2022

A Theory of Abstraction in Reinforcement Learning
David Abel
Reinforcement Learning Theoretical Understanding Cognitive Abstraction Optimal Behavior Effective Reinforcement Learning Adaptive Decision Making

January 15, 2022

Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection
Yuefei Lyu, Xiaoyu Yang, Jiaxin Liu, Philip S. Yu, Sihong Xie, Xi Zhang
Reinforcement Learning Policy OpTimization Interpretable Way Rumor Detection Model Attack Interpretable Policy Effective Reinforcement Learning

Effective Reinforcement Learning

Papers

Trapezoidal Gradient Descent for Effective Reinforcement Learning in Spiking Networks

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning

Effective Reinforcement Learning Based on Structural Information Principles

Behind the Myth of Exploration in Policy Gradients

Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes

Benchmarking Potential Based Rewards for Learning Humanoid Locomotion

Reinforcement learning with experience replay and adaptation of action dispersion

When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning

Phasic Self-Imitative Reduction for Sparse-Reward Goal-Conditioned Reinforcement Learning

Concurrent Credit Assignment for Data-efficient Reinforcement Learning

Task-Induced Representation Learning

A Theory of Abstraction in Reinforcement Learning

Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection