no code implementations • 12 Feb 2024 • Siyuan Li, Shijie Han, Yingnan Zhao, By Liang, Peng Liu
To achieve automatic auxiliary reward generation, we propose a novel representation learning approach that can measure the ``transition distance'' between states.
no code implementations • 1 Feb 2022 • Ke Sun, Yingnan Zhao, Wulong Liu, Bei Jiang, Linglong Kong
The empirical success of distributional reinforcement learning~(RL) highly depends on the distribution representation and the choice of distribution divergence.
no code implementations • NeurIPS 2021 • Ke Sun, Yafei Wang, Yi Liu, Yingnan Zhao, Bo Pan, Shangling Jui, Bei Jiang, Linglong Kong
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL.
no code implementations • 7 Oct 2021 • Ke Sun, Yingnan Zhao, Enze Shi, Yafei Wang, Xiaodong Yan, Bei Jiang, Linglong Kong
The theoretical advantages of distributional reinforcement learning~(RL) over classical RL remain elusive despite its remarkable empirical performance.
no code implementations • 29 Sep 2021 • Ke Sun, Yingnan Zhao, Yi Liu, Enze Shi, Yafei Wang, Aref Sadeghi, Xiaodong Yan, Bei Jiang, Linglong Kong
Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation.
no code implementations • 29 Sep 2021 • Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong
In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training.
Distributional Reinforcement Learning reinforcement-learning +1
1 code implementation • 17 Sep 2021 • Ke Sun, Yingnan Zhao, Shangling Jui, Linglong Kong
In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training.
no code implementations • 1 Jan 2021 • Chenjia Bai, Lingxiao Wang, Peng Liu, Zhaoran Wang, Jianye Hao, Yingnan Zhao
However, such an approach is challenging in developing practical exploration algorithms for Deep Reinforcement Learning (DRL).
no code implementations • 17 Oct 2020 • Chenjia Bai, Peng Liu, Kaiyu Liu, Lingxiao Wang, Yingnan Zhao, Lei Han
Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded.