177 citations · 341 across the 19 of their papers we have counts for
19 papers
HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach
Shixi Lian, Yi Ma, Jinyi Liu +2
Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data. To address the issue of limited data…
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
Kai Zhao, Jianye Hao, Yi Ma +3
Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the pe…
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning
Xiaohan Hu, Yi Ma, Chenjun Xiao +2
One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not…
ERL-Re: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy Representation
Jianye Hao, Pengyi Li, Hongyao Tang +3
Deep Reinforcement Learning (Deep RL) and Evolutionary Algorithms (EA) are two major paradigms of policy optimization with distinct learning principles, i.e., gradient-based v.s. g…
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations
Tong Sang, Hongyao Tang, Yi Ma +5
Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments…
Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning
Tong Sang, Hongyao Tang, Jianye Hao +2
Value estimation is one key problem in Reinforcement Learning. Albeit many successes have been achieved by Deep Reinforcement Learning (DRL) in different fields, the underlying str…
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain
Jianye Hao, Tianpei Yang, Hongyao Tang +5
Deep Reinforcement Learning (DRL) and Deep Multi-agent Reinforcement Learning (MARL) have achieved significant successes across a wide range of domains, including game AI, autonomo…
HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
Boyan Li, Hongyao Tang, Yan Zheng +5
Discrete-continuous hybrid action space is a natural setting in many practical problems, such as robot control and game AI. However, most previous Reinforcement Learning (RL) works…
Free-form tumor synthesis in computed tomography images via richer generative adversarial network
Qiangguo Jin, Hui Cui, Changming Sun +2
The insufficiency of annotated medical imaging scans for cancer makes it challenging to train and validate data-hungry deep learning models in precision oncology. We propose a new…
Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images
Qiangguo Jin, Hui Cui, Changming Sun +3
The capability of generalization to unseen domains is crucial for deep learning models when considering real-world scenarios. However, current available medical image datasets, suc…
Addressing Action Oscillations through Learning Policy Inertia
Chen Chen, Hongyao Tang, Jianye Hao +2
Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective in a wide range of challenging decision making and control tasks. However, these methods typical…
Foresee then Evaluate: Decomposing Value Estimation with Latent Future Prediction
Hongyao Tang, Jianye Hao, Guangyong Chen +6
Value function is the central notion of Reinforcement Learning (RL). Value estimation, especially with function approximation, can be challenging since it involves the stochasticit…
What About Inputing Policy in Value Function: Policy Representation and Policy-extended Value Function Approximator
Hongyao Tang, Zhaopeng Meng, Jianye Hao +9
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement Learning (RL), which extends conventional value function approximator (VFA) to take as input not only…
Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management
Jianwen Sun, Yan Zheng, Jianye Hao +2
With the increasing popularity of electric vehicles, distributed energy generation and storage facilities in smart grid systems, an efficient Demand-Side Management (DSM) is urgent…
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Tianpei Yang, Jianye Hao, Zhaopeng Meng +8
Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing tran…
An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning
Tianpei Yang, Weixun Wang, Hongyao Tang +9
Transfer Learning has shown great potential to enhance single-agent Reinforcement Learning (RL) efficiency. Similarly, Multiagent RL (MARL) can also be accelerated if agents can sh…
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Hongyao Tang, Jianye Hao, Guangyong Chen +4
Value functions are crucial for model-free Reinforcement Learning (RL) to obtain a policy implicitly or guide the policy updates. Value estimation heavily depends on the stochastic…
Towards Efficient Detection and Optimal Response against Sophisticated Opponents
Tianpei Yang, Zhaopeng Meng, Jianye Hao +3
Multiagent algorithms often aim to accurately predict the behaviors of other agents and find a best response accordingly. Previous works usually assume an opponent uses a stationar…
Hierarchical Heuristic Learning towards Effcient Norm Emergence
Tianpei Yang, Jianye Hao, Zhaopeng Meng +2
Social norms serve as an important mechanism to regulate the behaviors of agents and to facilitate coordination among them in multiagent systems. One important research question is…