
An Adversarial Imitation Click Model for Information Retrieval
Modern information retrieval systems, including web search, ads placemen...
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Learning Symbolic Rules for Interpretable Deep Reinforcement Learning
Recent progress in deep reinforcement learning (DRL) can be largely attr...
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Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep ActorCritic Reinforcement Learning
Constructing agents with planning capabilities has long been one of the ...
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EventTriggered Multiagent Reinforcement Learning with Communication under Limitedbandwidth Constraint
Communicating with each other in a distributed manner and behaving as a ...
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Dynamic Horizon Value Estimation for Modelbased Reinforcement Learning
Existing modelbased value expansion methods typically leverage a world ...
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Dynamic Knapsack Optimization Towards Efficient MultiChannel Sequential Advertising
In Ecommerce, advertising is essential for merchants to reach their tar...
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TripleGAIL: A MultiModal Imitation Learning Framework with Generative Adversarial Nets
Generative adversarial imitation learning (GAIL) has shown promising res...
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Continuous Multiagent Control using Collective Behavior Entropy for LargeScale Home Energy Management
With the increasing popularity of electric vehicles, distributed energy ...
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Learning to Accelerate Heuristic Searching for LargeScale Maximum Weighted bMatching Problems in Online Advertising
Bipartite bmatching is fundamental in algorithm design, and has been wi...
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CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Learning disentanglement aims at finding a low dimensional representatio...
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Efficient Deep Reinforcement Learning through Policy Transfer
Transfer Learning (TL) has shown great potential to accelerate Reinforce...
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Learning When to Transfer among Agents: An Efficient Multiagent Transfer Learning Framework
Transfer Learning has shown great potential to enhance the singleagent ...
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KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge
Reinforcement learning agents usually learn from scratch, which requires...
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Qvalue Path Decomposition for Deep Multiagent Reinforcement Learning
Recently, deep multiagent reinforcement learning (MARL) has become a hig...
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Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning
In many realworld settings, a team of cooperative agents must learn to ...
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There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
Deep neural networks (DNN) are increasingly applied in safetycritical s...
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Diverse Behavior Is What Game AI Needs: Generating Varied HumanLike Playing Styles Using Evolutionary MultiObjective Deep Reinforcement Learning
Designing artificial intelligence for games (Game AI) has been long reco...
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Efficient meta reinforcement learning via meta goal generation
Meta reinforcement learning (metaRL) is able to accelerate the acquisit...
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Independent Generative Adversarial SelfImitation Learning in Cooperative Multiagent Systems
Many tasks in practice require the collaboration of multiple agents thro...
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From Few to More: Largescale Dynamic Multiagent Curriculum Learning
A lot of efforts have been devoted to investigating how agents can learn...
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Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
In multiagent systems (MASs), each agent makes individual decisions but ...
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Spectralbased Graph Convolutional Network for Directed Graphs
Graph convolutional networks(GCNs) have become the most popular approach...
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Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for modelfree Reinforcement Learning (RL) t...
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Deep MultiAgent Reinforcement Learning with DiscreteContinuous Hybrid Action Spaces
Deep Reinforcement Learning (DRL) has been applied to address a variety ...
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Hierarchical Deep Multiagent Reinforcement Learning
Despite deep reinforcement learning has recently achieved great successe...
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SCCrFMQ Learning in Cooperative Markov Games with Continuous Actions
Although many reinforcement learning methods have been proposed for lear...
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BayesToMoP: A Fast Detection and Best Response Algorithm Towards Sophisticated Opponents
Multiagent algorithms often aim to accurately predict the behaviors of o...
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Learning to Advertise with Adaptive Exposure via Constrained TwoLevel Reinforcement Learning
For online advertising in ecommerce, the traditional problem is to assi...
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An Optimal Rewiring Strategy for Reinforcement Social Learning in Cooperative Multiagent Systems
Multiagent coordination in cooperative multiagent systems (MASs) has bee...
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Falsification of CyberPhysical Systems Using Deep Reinforcement Learning
With the rapid development of software and distributed computing, Cyber...
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Hierarchical Heuristic Learning towards Effcient Norm Emergence
Social norms serve as an important mechanism to regulate the behaviors o...
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SAIGA: A Multiagent Reinforcement Learning Method Towards Socially Optimal Outcomes
In multiagent environments, the capability of learning is important for ...
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Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent Reinforcement Learning Approach
The Iterated Prisoner's Dilemma has guided research on social dilemmas f...
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Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments
Despite single agent deep reinforcement learning has achieved significan...
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Blind Image Denoising via Dependent Dirichlet Process Tree
Most existing image denoising approaches assumed the noise to be homogen...
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Jianye Hao
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