30 citations · 56 across the 7 of their papers we have counts for
7 papers
Long-Run Conditional Value at Risk Reinforcement Learning
Qixin Wang, Hao Cao, Jian-Qiang Hu +2
We propose a reinforcement learning method for minimizing the long-run conditional value at risk (CVaR) in Markov decision processes with finite state and action spaces. The method…
Distributionally Robust Offline Reinforcement Learning with Linear Function Approximation
Xiaoteng Ma, Zhipeng Liang, Jose Blanchet +5
Among the reasons hindering reinforcement learning (RL) applications to real-world problems, two factors are critical: limited data and the mismatch between the testing environment…
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning
Xiaoteng Ma, Shuai Ma, Li Xia +1
Keeping risk under control is often more crucial than maximizing expected rewards in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The…
A unified algorithm framework for mean-variance optimization in discounted Markov decision processes
Shuai Ma, Xiaoteng Ma, Li Xia
This paper studies the risk-averse mean-variance optimization in infinite-horizon discounted Markov decision processes (MDPs). The involved variance metric concerns reward variabil…
Average-Reward Reinforcement Learning with Trust Region Methods
Xiaoteng Ma, Xiaohang Tang, Li Xia +2
Most of reinforcement learning algorithms optimize the discounted criterion which is beneficial to accelerate the convergence and reduce the variance of estimates. Although the dis…
Wasserstein Distance guided Adversarial Imitation Learning with Reward Shape Exploration
Ming Zhang, Yawei Wang, Xiaoteng Ma +4
The generative adversarial imitation learning (GAIL) has provided an adversarial learning framework for imitating expert policy from demonstrations in high-dimensional continuous t…
DSAC: Distributional Soft Actor-Critic for Risk-Sensitive Reinforcement Learning
Xiaoteng Ma, Junyao Chen, Li Xia +3
We present Distributional Soft Actor-Critic (DSAC), a distributional reinforcement learning (RL) algorithm that combines the strengths of distributional information of accumulated…