22 citations · 24 across the 6 of their papers we have counts for
6 papers
Auxiliary Reward Generation with Transition Distance Representation Learning
Siyuan Li, Shijie Han, Yingnan Zhao +2
Reinforcement learning (RL) has shown its strength in challenging sequential decision-making problems. The reward function in RL is crucial to the learning performance, as it serve…
Distributional Reinforcement Learning with Regularized Wasserstein Loss
Ke Sun, Yingnan Zhao, Wulong Liu +2
The empirical success of distributional reinforcement learning (RL) highly relies on the choice of distribution divergence equipped with an appropriate distribution representation.…
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Ke Sun, Yafei Wang, Yi Liu +5
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL. Despite its…
Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun, Yingnan Zhao, Enze Shi +4
The remarkable empirical performance of distributional reinforcement learning (RL) has garnered increasing attention to understanding its theoretical advantages over classical RL.…
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations
Ke Sun, Yingnan Zhao, Shangling Jui +1
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…
Variational Dynamic for Self-Supervised Exploration in Deep Reinforcement Learning
Chenjia Bai, Peng Liu, Kaiyu Liu +3
Efficient exploration remains a challenging problem in reinforcement learning, especially for tasks where extrinsic rewards from environments are sparse or even totally disregarded…