Reinforcement Learning

Reinforcement learning (RL) focuses on training agents to make optimal decisions in an environment by learning through trial and error, aiming to maximize cumulative rewards. Current research emphasizes improving RL's efficiency and robustness, particularly in areas like human-in-the-loop training (e.g., using human feedback to refine models), handling uncertainty and sparse rewards, and scaling to complex tasks (e.g., robotics, autonomous driving). Prominent approaches involve various policy gradient methods, Monte Carlo Tree Search, and the integration of large language models for improved decision-making and task decomposition. These advancements are driving progress in diverse fields, including robotics, game playing, and the development of more human-aligned AI systems.

Papers

October 16, 2024

When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter
Yansong Li, Zeyu Dong, Ertai Luo, Yu Wu, Shuo Wu, Shuo Han
Reinforcement Learning LeArning Abstract Raw Data Positive Reinforcement Free Learning Model Free Algorithm Data Filtering Dynamic Algorithm Model Based Policy
Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration
Yiannis Kantaros, Jun Wang
Reinforcement Learning Markov Decision Process Temporal Logic Task Specification Sample Efficient Reinforcement Learning Guided Exploration

October 15, 2024

Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions
Ayush Jain, Norio Kosaka, Xinhu Li, Kyung-Min Kim, Erdem Bıyık, Joseph J. Lim
Reinforcement Learning Complexity Level Q Function Policy Actor Critic Deterministic Policy Gradient Sub Optimality Gap
Zero-shot Model-based Reinforcement Learning using Large Language Models
Abdelhakim Benechehab, Youssef Attia El Hili, Ambroise Odonnat, Oussama Zekri, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Ievgen Redko, Balázs Kégl
Reinforcement Learning Zero Shot Disentangled Learning Continuous Time Markov Decision Process
BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
Hikaru Shindo, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting
Reinforcement Learning New Framework Symbolic Reasoning Neural Agent Deep Policy Symbolic Policy
Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents
Federico Pizarro Bejarano, Lukas Brunke, Angela P. Schoellig
Reinforcement Learning Training Data High Efficiency System Performance Reinforcement Learning Agent Safety Filter Standard Reinforcement Learning
Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Search
Jiamian Li
Reinforcement Learning Monte Carlo Tree Search Reward Shaping Q Function Imperfect Information Game Double Deep Q Learning Value Estimation Fictitious Self Play
SDS -- See it, Do it, Sorted: Quadruped Skill Synthesis from Single Video Demonstration
Jeffrey Li, Maria Stamatopoulou, Dimitrios Kanoulas
Reinforcement Learning Reward Function Proximal Policy Optimization Single Label Quadruped Robot Video Demonstration Auxiliary Sorting Network Rearranges State of the Art Imitation
Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement
Zhi Wang, Li Zhang, Wenhao Wu, Yuanheng Zhu, Dongbin Zhao, Chunlin Chen
Reinforcement Learning World Event Artificial General Intelligence Decision Transformer Sequence Modeling Offline Meta Reinforcement Learning Possible World
Visual Manipulation with Legs
Xialin He, Chengjing Yuan, Wenxuan Zhou, Ruihan Yang, David Held, Xiaolong Wang
Reinforcement Learning Image Editing Human Like Locomotion Behavior Loco Manipulation Insect Leg
Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task
Yunho Kim, Jaehyun Park, Heejun Kim, Sejin Kim, Byung-Jun Lee, Sundong Kim
Reinforcement Learning Offline Reinforcement Learning Q Learning Decision Making Related Task Strategic Ability
ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis
Abhijit Manatkar, Devarsh Patel, Hima Patel, Naresh Manwani
Reinforcement Learning Imitation Learning Constructive Approach Adversarial Imitation Learning Exploratory Data Analysis Exploratory Learning EDA Developer
Unveiling Options with Neural Decomposition
Mahdi Alikhasi, Levi H. S. Lelis
Reinforcement Learning Markov Decision Process Health Policy Recommendation Learning Decomposition Unveiling Camouflaged Object Oblique Decision Tree
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
Jiaheng Hu, Zizhao Wang, Peter Stone, Roberto Martín-Martín
Reinforcement Learning Hierarchical Reinforcement Learning Skill Discovery Hierarchical Framework Skill Mention
Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning
Jiayu Chen, Wentse Chen, Jeff Schneider
Reinforcement Learning Offline Reinforcement Learning Model Based Reinforcement Learning Action Free Offline
Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment
Peter Vamplew, Conor F Hayes, Cameron Foale, Richard Dazeley, Hadassah Harland
Reinforcement Learning Artificial Intelligence BRIO Tool Multi Objective Reinforcement Learning Vector Valued Pluralistic Alignment
Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets
Jinyu Liu, Hongye Guo, Yun Li, Qinghu Tang, Fuquan Huang, Tunan Chen, Haiwang Zhong, Qixin Chen
Reinforcement Learning High Dimensional High Dimension Paper Bidding

Reinforcement Learning

Papers

When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter

Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration

Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functions

Zero-shot Model-based Reinforcement Learning using Large Language Models

BlendRL: A Framework for Merging Symbolic and Neural Policy Learning

Safety Filtering While Training: Improving the Performance and Sample Efficiency of Reinforcement Learning Agents

Improve Value Estimation of Q Function and Reshape Reward with Monte Carlo Tree Search

SDS -- See it, Do it, Sorted: Quadruped Skill Synthesis from Single Video Demonstration

Meta-DT: Offline Meta-RL as Conditional Sequence Modeling with World Model Disentanglement

Visual Manipulation with Legs

Diffusion-Based Offline RL for Improved Decision-Making in Augmented ARC Task

ILAEDA: An Imitation Learning Based Approach for Automatic Exploratory Data Analysis

Unveiling Options with Neural Decomposition

Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning

Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning

Multi-objective Reinforcement Learning: A Tool for Pluralistic Alignment

Reinforcement Learning Based Bidding Framework with High-dimensional Bids in Power Markets

Action Gaps and Advantages in Continuous-Time Distributional Reinforcement Learning

Improving the Language Understanding Capabilities of Large Language Models Using Reinforcement Learning

Transforming Game Play: A Comparative Study of DCQN and DTQN Architectures in Reinforcement Learning