Search Results - "Bai, Chenjia"

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain by Hao, Jianye, Yang, Tianpei, Tang, Hongyao, Bai, Chenjia, Liu, Jinyi, Meng, Zhaopeng, Liu, Peng, Wang, Zhen

“…Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL) have achieved significant success across a wide range of domains, including…”
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Diverse randomized value functions: A provably pessimistic approach for offline reinforcement learning by Yu, Xudong, Bai, Chenjia, Guo, Hongyi, Wang, Changhong, Wang, Zhen

“…Offline Reinforcement Learning (RL) faces challenges such as distributional shift and unreliable value estimation, especially for out-of-distribution (OOD)…”
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Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning by Bai, Chenjia, Liu, Peng, Liu, Kaiyu, Wang, Lingxiao, Zhao, Yingnan, Han, Lei, Wang, Zhaoran

“…Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or…”
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False Correlation Reduction for Offline Reinforcement Learning by Deng, Zhihong, Fu, Zuyue, Wang, Lingxiao, Yang, Zhuoran, Bai, Chenjia, Zhou, Tianyi, Wang, Zhaoran, Jiang, Jing

“…Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems. Most existing papers only discuss…”
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Pessimistic value iteration for multi-task data sharing in Offline Reinforcement Learning by Bai, Chenjia, Wang, Lingxiao, Hao, Jianye, Yang, Zhuoran, Zhao, Bin, Wang, Zhen, Li, Xuelong

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Skill matters: Dynamic skill learning for multi-agent cooperative reinforcement learning by Li, Tong, Bai, Chenjia, Xu, Kang, Chu, Chen, Zhu, Peican, Wang, Zhen

“…With the popularization of intelligence, the necessity of cooperation between intelligent machines makes the research of collaborative multi-agent…”
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Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning by Bai, Chenjia, Xiao, Ting, Zhu, Zhoufan, Wang, Lingxiao, Zhou, Fan, Garg, Animesh, He, Bin, Liu, Peng, Wang, Zhaoran

“…A key challenge in offline reinforcement learning (RL) is how to ensure the learned offline policy is safe, especially in safety-critical domains. In this…”
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Ensemble successor representations for task generalization in offline-to-online reinforcement learning by Wang, Changhong, Yu, Xudong, Bai, Chenjia, Zhang, Qiaosheng, Wang, Zhen

“…In reinforcement learning (RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently,…”
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Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness by Wen, Xiaoyu, Yu, Xudong, Yang, Rui, Chen, Haoyuan, Bai, Chenjia, Wang, Zhen

“…To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a promising approach involves the combination of offline RL, which…”
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Guided goal generation for hindsight multi-goal reinforcement learning by Bai, Chenjia, Liu, Peng, Zhao, Wei, Tang, Xianglong

“…Typical reinforcement learning (RL) can only perform a single task and thus cannot scale to problems for which an agent needs to perform multiple tasks, such…”
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Elucidation of the using condition and working mechanism of tertiary lactose in dry powder formulations for inhalation by Ye, Yuqing, Shi, Tingting, Fan, Ziyi, Bai, Chenjia, Ma, Ying, Zhu, Jesse

“…Tertiary lactose has been shown to improve aerosolization performance of carrier-based dry powder inhalation formulations. The working mechanism of the…”
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Addressing Hindsight Bias in Multigoal Reinforcement Learning by Bai, Chenjia, Wang, Lingxiao, Wang, Yixin, Wang, Zhaoran, Zhao, Rui, Bai, Chenyao, Liu, Peng

“…Multigoal reinforcement learning (RL) extends the typical RL with goal-conditional value functions and policies. One efficient multigoal RL algorithm is the…”
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Obtaining accurate estimated action values in categorical distributional reinforcement learning by Zhao, Yingnan, Liu, Peng, Bai, Chenjia, Zhao, Wei, Tang, Xianglong

“…Categorical Distributional Reinforcement Learning (CDRL) uses a categorical distribution with evenly spaced outcomes to model the entire distribution of…”
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Generating attentive goals for prioritized hindsight reinforcement learning by Liu, Peng, Bai, Chenjia, Zhao, Yingnan, Bai, Chenyao, Zhao, Wei, Tang, Xianglong

“…Typical reinforcement learning (RL) performs a single task and does not scale to problems in which an agent must perform multiple tasks, such as moving a robot…”
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Self-Supervised Imitation for Offline Reinforcement Learning With Hindsight Relabeling by Yu, Xudong, Bai, Chenjia, Wang, Changhong, Yu, Dengxiu, Chen, C. L. Philip, Wang, Zhen

“…Reinforcement learning (RL) requires a lot of interactions with the environment, which is usually expensive or dangerous in real-world tasks. To address this…”
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Forward KL Regularized Preference Optimization for Aligning Diffusion Policies by Shan, Zhao, Fan, Chenyou, Qiu, Shuang, Shi, Jiyuan, Bai, Chenjia

“…Diffusion models have achieved remarkable success in sequential decision-making by leveraging the highly expressive model capabilities in policy learning. A…”
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Cross-Domain Policy Adaptation by Capturing Representation Mismatch by Lyu, Jiafei, Bai, Chenjia, Yang, Jingwen, Lu, Zongqing, Li, Xiu

“…It is vital to learn effective policies that can be transferred to different domains with dynamics discrepancies in reinforcement learning (RL). In this paper,…”
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Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning by Wang, Changhong, Yu, Xudong, Bai, Chenjia, Zhang, Qiaosheng, Wang, Zhen

“…In Reinforcement Learning (RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently,…”
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Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning by Zhang, Qiaosheng, Bai, Chenjia, Hu, Shuyue, Wang, Zhen, Li, Xuelong

“…This work designs and analyzes a novel set of algorithms for multi-agent reinforcement learning (MARL) based on the principle of information-directed sampling…”
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Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning by Yu, Xudong, Bai, Chenjia, Guo, Hongyi, Wang, Changhong, Wang, Zhen

“…Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions. To address…”
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