Article-Journal

Active Sampling for Deep Q-learning Based on TD-error Adaptive Correction.

Journal of Computer Research and Development (in Chinese), 2019

We propose an active sampling method based on TD-error adaptive correction in order to solve sample efficiency problem in deep Q-learning.

Chenjia Bai , Peng Liu , Wei Zhao , Xianglong Tang

Active Sampling for Deep Q-learning Based on TD-error Adaptive Correction.

Obtaining Accurate Estimated Action Values in Categorical Distributional Reinforcement Learning.

Knowledge-Based Systems (KBS), 2020

This paper describes a method of obtaining more accurate estimated action values for CDRL using adaptive bounds.

Yingnan Zhao , Peng Liu , Chenjia Bai , Wei Zhao , Xianglong Tang

Obtaining Accurate Estimated Action Values in Categorical Distributional Reinforcement Learning.

Generating Attentive Goals for Prioritized Hindsight Reinforcement Learning.

Knowledge-Based Systems (KBS), 2020

We propose a novel prioritized hindsight model for multi-goal RL in which the agent is provided with more valuable goals, as measured by the expected temporal-difference (TD) error.

Peng Liu , Chenjia Bai , Yingnan Zhao , Chenyao Bai , Wei Zhao , Xianglong Tang

Generating Attentive Goals for Prioritized Hindsight Reinforcement Learning.

Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning.

IEEE Transactions on Neural Networks and Learning Systems, 2021

We propose a variational dynamic model based on the conditional variational inference to model the multimodality and stochasticity.

Chenjia Bai , Peng Liu , Kaiyu Liu , Lingxiao Wang , Yingnan Zhao , Lei Han

Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning.

Addressing Hindsight Bias in Multi-Goal Reinforcement Learning.

IEEE Transactions on Cybernetics, 2021

We analyze the hindsight bias due to this use of hindsight goals and propose the bias-corrected HER (BHER), an efficient algorithm that corrects the hindsight bias in training.

Chenjia Bai , Lingxiao Wang , Yixin Wang , Rui Zhao , Chenyao Bai , Peng Liu

Addressing Hindsight Bias in Multi-Goal Reinforcement Learning.

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain.

IEEE Transactions on Neural Networks and Learning Systems, 2022

We conduct a comprehensive survey on existing exploration methods for both single-agent RL and multiagent RL.

Jianye Hao , Tianpei Yang , Hongyao Tang , Chenjia Bai , Jinyi Liu , Zhaopeng Meng , Peng Liu , Zhen Wang

Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain.

Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning.

IEEE Transactions on Neural Networks and Learning Systems, 2022

We propose monotonic quantile network (MQN) with conservative quantile regression (CQR) for risk-averse policy learning.

Chenjia Bai , Ting Xiao , Zhoufan Zhu , Lingxiao Wang , Fan Zhou , Peng Liu

Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning.

Self-Supervised Imitation for Offline Reinforcement Learning with Hindsight Relabeling.

IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2022

We present an offline RL algorithm that combines hindsight relabeling and supervised regression to predict actions without oracle information.

Xudong Yu , Chenjia Bai , Changhong Wang , Dengxiu Yu , C. L. Philip Chen , Zhen Wang^✉

Self-Supervised Imitation for Offline Reinforcement Learning with Hindsight Relabeling.

Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning.

In Information Sciences, 2023

We introduce a novel strategy employing diverse randomized value functions to estimate the posterior distribution of Q-values.

Xudong Yu , Chenjia Bai^✉ , Hongyi Guo , Changhong Wang^✉ , Zhen Wang

Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning.

Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning.

In Artificial Intelligence (AIJ), 2023

We propose an uncertainty-based MTDS approach that shares the entire dataset without data selection.

Chenjia Bai , Lingxiao Wang , Jianye Hao , Zhuoran Yang , Bin Zhao , Zhen Wang^✉ , Xuelong Li^✉

Pessimistic Value Iteration for Multi-Task Data Sharing in Offline Reinforcement Learning.