天津大学深度强化学习实验室
Tianjin University-Deep Reinforcement Learning Lab
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Our lab has several Ph.D. and Master positions. If you are interested in our research, please send us your CV
(jianye.hao@tju.edu.cn / yanzheng@tju.edu.cn)
实验室长期接受优秀同学交流学习,攻读硕士/博士学位的同学加入。同时欢迎感兴趣学部(院)夏令营活动的同学进行邮件联系!
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新闻
News
Jan 21, 2023:
Seven papers accepted by ICLR 2023:
"ERL-Re2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation", "Breaking the Curse of Dimensionality in Multiagent State Space: A Unified Agent Permutation Framework", "EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model", "Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection", "DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks", "CFlowNets: Continuous control with Generative Flow Network", "Out-of-distribution Detection with Implicit Outlier Transformation"
Nov 25, 2022:
Four papers accepted by AAAI 2023:
"SplitNet: A Reinforcement Learning based Sequence Splitting Method for the MinMax Multiple Travelling Salesman Problem", "Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network", "Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems", "Models as Agents: Optimizing Muti-Step Predictions of interactive Local Models in Model-Based Multi-Agent Reinforcement Learning"
Sep 15, 2022:
Seven papers accepted by NeurIPS 2022:
"Multiagent Q-learning with Sub-Team Coordination", "Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing", "GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis", "Versatile Multi-stage Graph Neural Network for Circuit Representation", "The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design", "DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning", "Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning"
May 18, 2022:
Five papers accepted by ICML 2022:
"PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information Collaboration", "Individual Reward Assisted Multi-Agent Reinforcement Learning", "Plan Your Target and Learn Your Skills: Transferable State-Only lmitation Learning via Decoupled Policy Optimization", "Neuro-Symbolic Hierarchical Rule Induction", "Learning Pseudometric-based Action Representations for Offline Reinforcement Learning"
Apr 21, 2022:
One paper accepted by IJCAI 2022:
"PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations"
Jan 28, 2022:
Three papers accepted by ICLR 2022:
"HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation", "Online Ad Hoc Teamwork under Partial Observability", "Learning State Representations via Retracing in Reinforcement Learning"
Dec 1, 2021:
One paper accepted by AAAI 2022:
"What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator"
Nov 9, 2021:
Runner-up, Best Paper Award in PRICAI 2021:
"Detecting and Learning Against Unknown Opponents for Automated Negotiations"
Oct 1, 2021:
Six papers accepted by NeurIPS 2021:
"Dynamic Bottleneck for Robust Self-Supervised Exploration", "Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning", "Model-Based Reinforcement Learning via Imagination with Derived Memory", "A Reinforcement Learning Based Bi-level Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems", "Adaptive Online Packing-guided Search for POMDPs", "An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning"
Jun 5, 2021:
One paper accepted by ICML 2021:
" Principled Exploration via Optimistic Bootstrapping and Backward Induction"
May 22, 2021:
One paper accepted by KDD 2021:
" A Multi-Graph Attributed Reinforcement Learning based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling Problem"
Apr 30, 2021:
One paper accepted by IJCAI 2021:
" Ordering-Based Causal Discovery with Reinforcement Learning"
Apr 27, 2021:
One paper accepted by IEEE Transaction on Smart Grid 2021:
" Vulnerability Assessment of Deep Reinforcement Learning Models for Power System Topology Optimization"
Jan 1, 2021:
One paper accepted by ICSE 2021:
" Automatic Web Testing using Curiosity-Driven Reinforcement Learning"
Dec 4, 2020:
Three papers accepted by AAAI 2021:
"Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction","Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning","Addressing Action Oscillations through Learning Policy Inertia"
Sep 30, 2020:
One paper accepted by NeurIPS 2020:
"Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping"
Jun 3, 2020:
Two papers accepted by ICML 2020:
"Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising","Q-value Path Decomposition for Deep Multiagent Reinforcement Learning"
Apr 21, 2020:
Five papers accepted by IJCAI 2020:
"Learning to Accelerate Heuristic Searching for Large-Scale MaximumWeighted b-Matching Problems in Online Advertising", "Efficient Deep Reinforcement Learning via Adaptive Policy Transfer ", " Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objectives Deep Reinforcement Learning", " Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets", "KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge"
Oct 22, 2019:
ACM SIGSOFT Distinguished Paper Award in ASE 2019:
" Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning"
Oct 15, 2019:
Best Paper Award in DAI 2019:
" Achieving Cooperation Through Deep Multiagent Reinforcement Learning in Sequential Prisoner's Dilemmas"
最新成果
Recent Work
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