Reinforcement Learning Papers
1.0.0
Welcome to our GitHub repository! This repository is dedicated to curating significant research papers in the field of Reinforcement Learning (RL) that have been accepted at top academic conferences such as AAAI, IJCAI, NeurIPS, ICML, ICLR, ICRA, AAMAS and more. We provide you with a convenient resource hub to help you stay updated on the latest developments in reinforcement learning, delve into research trends, and explore cutting-edge algorithms and methods.
Markdown format：
- **Paper Name**.
[[pdf](link)]
[[code](link)]
- Author 1, Author 2, and Author 3. *conference, year*.
Please help to contribute this list by contacting me or add pull request.
For any questions, feel free to contact me ?.
Online Tuning for Offline Decentralized Multi-Agent Reinforcement Learning. [pdf]
Reward Poisoning Attacks on Offline Multi-Agent Reinforcement Learning. [pdf]
Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning. [pdf]
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning. [pdf]
Quantum Multi-Agent Meta Reinforcement Learning. [pdf]
Learning Explicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning via Polarization Policy Gradient. [pdf]
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning. [pdf]
DM²: Decentralized Multi-Agent Reinforcement Learning via Distribution Matching. [pdf]
Consensus Learning for Cooperative Multi-Agent Reinforcement Learning. [pdf]
HAVEN: Hierarchical Cooperative Multi-Agent Reinforcement Learning with Dual Coordination Mechanism. [pdf]
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning. [pdf]
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning. [pdf]
Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning With Applications to Ridesharing. [pdf]
Tackling Safe and Efficient Multi-Agent Reinforcement Learning via Dynamic Shielding (Student Abstract). [pdf]
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement. [pdf]
Adaptive Learning Rates for Multi-Agent Reinforcement Learning. [pdf]
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning. [pdf]
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning. [pdf]
Mediated Multi-Agent Reinforcement Learning. [pdf]
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement Learning. [pdf]
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning. [pdf]
Learning Structured Communication for Multi-Agent Reinforcement Learning. [pdf]
Model-based Sparse Communication in Multi-agent Reinforcement Learning. [pdf]
Sequential Cooperative Multi-Agent Reinforcement Learning. [pdf]
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration. [pdf]
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning. [pdf]
CraftEnv: A Flexible Collective Robotic Construction Environment for Multi-Agent Reinforcement Learning. [pdf]
Multi-Agent Reinforcement Learning with Safety Layer for Active Voltage Control. [pdf]
Model-based Dynamic Shielding for Safe and Efficient Multi-agent Reinforcement Learning. [pdf]
Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning. [pdf]
Counterexample-Guided Policy Refinement in Multi-Agent Reinforcement Learning. [pdf]
Prioritized Tasks Mining for Multi-Task Cooperative Multi-Agent Reinforcement Learning. [pdf]
TransfQMix: Transformers for Leveraging the Graph Structure of Multi-Agent Reinforcement Learning Problems. [pdf]
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning. [pdf]
Towards Explaining Sequences of Actions in Multi-Agent Deep Reinforcement Learning Models. [pdf]
Multi-Agent Deep Reinforcement Learning for High-Frequency Multi-Market Making. [pdf]
Learning Individual Difference Rewards in Multi-Agent Reinforcement Learning. [pdf]
Off-Beat Multi-Agent Reinforcement Learning. [pdf]
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning. [pdf]
Off-the-Grid MARL: Datasets and Baselines for Offline Multi-Agent Reinforcement Learning. [pdf]
Grey-box Adversarial Attack on Communication in Multi-agent Reinforcement Learning. [pdf]
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads. [pdf]
Learning to Self-Reconfigure for Freeform Modular Robots via Altruism Multi-Agent Reinforcement Learning. [pdf]
Multi-Agent Path Finding via Reinforcement Learning with Hybrid Reward. [pdf]
Learning Solutions in Large Economic Networks using Deep Multi-Agent Reinforcement Learning. [pdf]
Offline Multi-Agent Reinforcement Learning with Coupled Value Factorization. [pdf]
Causality Detection for Efficient Multi-Agent Reinforcement Learning. [pdf]
Attention-Based Recurrency for Multi-Agent Reinforcement Learning under State Uncertainty. [pdf]
Fair Transport Network Design using Multi-Agent Reinforcement Learning. [pdf]
Reinforcement Learning in Multi-Objective Multi-Agent Systems. [pdf]
Enhancing Smart, Sustainable Mobility with Game Theory and Multi-Agent Reinforcement Learning. [pdf]
Stateful Active Facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning. [pdf]
MACTA: A Multi-agent Reinforcement Learning Approach for Cache Timing Attacks and Detection. [pdf]
MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning. [pdf]
Scaling Laws for a Multi-Agent Reinforcement Learning Model. [pdf]
RPM: Generalizable Multi-Agent Policies for Multi-Agent Reinforcement Learning. [pdf]
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning. [pdf]
Order Matters: Agent-by-agent Policy Optimization. [pdf]
Context-Aware Bayesian Network Actor-Critic Methods for Cooperative Multi-Agent Reinforcement Learning. [pdf]
Entity Divider with Language Grounding in Multi-Agent Reinforcement Learning. [pdf]
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning. [pdf]
An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning. [pdf]
RACE: Improve Multi-Agent Reinforcement Learning with Representation Asymmetry and Collaborative Evolution. [pdf]
Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning. [pdf]
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation. [pdf]
Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation. [pdf]
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability. [pdf]
Complementary Attention for Multi-Agent Reinforcement Learning. [pdf]
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. [pdf]
Multi-Target Pursuit by a Decentralized Heterogeneous UAV Swarm using Deep Multi-Agent Reinforcement Learning. [pdf]
Explainable Action Advising for Multi-Agent Reinforcement Learning. [pdf]
Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios. [pdf]
Conflict-constrained Multi-agent Reinforcement Learning Method for Parking Trajectory Planning. [pdf]
Explainable Multi-Agent Reinforcement Learning for Temporal Queries. [pdf]
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism. [pdf]
Learning to Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching and Rescheduling via Reinforcement Learning. [pdf]
Decentralized Anomaly Detection in Cooperative Multi-Agent Reinforcement Learning. [pdf]
GPLight: Grouped Multi-agent Reinforcement Learning for Large-scale Traffic Signal Control. [pdf]
Deep Hierarchical Communication Graph in Multi-Agent Reinforcement Learning. [pdf]
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement Learning. [pdf]
Inducing Stackelberg Equilibrium through Spatio-Temporal Sequential Decision-Making in Multi-Agent Reinforcement Learning. [pdf]
Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning. [pdf]
MA2CL: Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning. [pdf]
Competitive-Cooperative Multi-Agent Reinforcement Learning for Auction-based Federated Learning. [pdf]
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning. [pdf]
If you use this toolbox in your research, please cite this project.
@misc{YalunAwesome,
author = {Yalun Wu},
title = {Reinforcement-Learning-Papers},
year = {2023},
howpublished = {url{https://github.com/Allenpandas/Reinforcement-Learning-Papers}}
}
