36 citations · 91 across the 20 of their papers we have counts for
15 papers · 1 filter
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
Chengxing Jia, Fuxiang Zhang, Yi-Chen Li +5
Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset. For precise and efficient task identificatio…
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
Xinyu Zhang, Wenjie Qiu, Yi-Chen Li +4
Developing policies that can adjust to non-stationary environments is essential for real-world reinforcement learning applications. However, learning such adaptable policies in off…
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data Limitations
Renzhe Zhou, Chen-Xiao Gao, Zongzhang Zhang +1
Generalization and sample efficiency have been long-standing issues concerning reinforcement learning, and thus the field of Offline Meta-Reinforcement Learning~(OMRL) has gained i…
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
Xiong-Hui Chen, Junyin Ye, Hang Zhao +9
Imitation learning (IL) enables agents to mimic expert behaviors. Most previous IL techniques focus on precisely imitating one policy through mass demonstrations. However, in many…
ACT: Empowering Decision Transformer with Dynamic Programming via Advantage Conditioning
Chen-Xiao Gao, Chenyang Wu, Mingjun Cao +3
Decision Transformer (DT), which employs expressive sequence modeling techniques to perform action generation, has emerged as a promising approach to offline policy optimization. H…
Policy Regularization with Dataset Constraint for Offline Reinforcement Learning
Yuhang Ran, Yi-Chen Li, Fuxiang Zhang +2
We consider the problem of learning the best possible policy from a fixed dataset, known as offline Reinforcement Learning (RL). A common taxonomy of existing offline RL works is p…
How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement
Xu-Hui Liu, Feng Xu, Xinyu Zhang +5
Imitation learning aims to mimic the behavior of experts without explicit reward signals. Passive imitation learning methods which use static expert datasets typically suffer from…
Efficient Communication via Self-supervised Information Aggregation for Online and Offline Multi-agent Reinforcement Learning
Cong Guan, Feng Chen, Lei Yuan +2
Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). Previous works typically combine raw messages of teammates with…
Multi-agent Dynamic Algorithm Configuration
Ke Xue, Jiacheng Xu, Lei Yuan +4
Automated algorithm configuration relieves users from tedious, trial-and-error tuning tasks. A popular algorithm configuration tuning paradigm is dynamic algorithm configuration (D…
Deep Anomaly Detection and Search via Reinforcement Learning
Chao Chen, Dawei Wang, Feng Mao +2
Semi-supervised Anomaly Detection (AD) is a kind of data mining task which aims at learning features from partially-labeled datasets to help detect outliers. In this paper, we clas…
Model Generation with Provable Coverability for Offline Reinforcement Learning
Chengxing Jia, Hao Yin, Chenxiao Gao +4
Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could ad…
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
Cong Fei, Bin Wang, Yuzheng Zhuang +5
Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However,…
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…
Monte-Carlo Tree Search for Policy Optimization
Xiaobai Ma, Katherine Driggs-Campbell, Zongzhang Zhang +1
Gradient-based methods are often used for policy optimization in deep reinforcement learning, despite being vulnerable to local optima and saddle points. Although gradient-free met…
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Hongyao Tang, Jianye Hao, Tangjie Lv +8
Multiagent reinforcement learning (MARL) is commonly considered to suffer from non-stationary environments and exponentially increasing policy space. It would be even more challeng…