32 citations · 177 across the 32 of their papers we have counts for
32 papers
Towards Generalizable Reinforcement Learning for Trade Execution
Chuheng Zhang, Yitong Duan, Xiaoyu Chen +3
Optimized trade execution is to sell (or buy) a given amount of assets in a given time with the lowest possible trading cost. Recently, reinforcement learning (RL) has been applied…
Asking Before Acting: Gather Information in Embodied Decision Making with Language Models
Xiaoyu Chen, Shenao Zhang, Pushi Zhang +2
With strong capabilities of reasoning and a broad understanding of the world, Large Language Models (LLMs) have demonstrated immense potential in building versatile embodied decisi…
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
Xiaoyu Chen, Xiangming Zhu, Yufeng Zheng +8
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluct…
Reinforcement learning with Demonstrations from Mismatched Task under Sparse Reward
Yanjiang Guo, Jingyue Gao, Zheng Wu +2
Reinforcement learning often suffer from the sparse reward issue in real-world robotics problems. Learning from demonstration (LfD) is an effective way to eliminate this problem, w…
Safe Model-Based Reinforcement Learning with an Uncertainty-Aware Reachability Certificate
Dongjie Yu, Wenjun Zou, Yujie Yang +4
Safe reinforcement learning (RL) that solves constraint-satisfactory policies provides a promising way to the broader safety-critical applications of RL in real-world problems such…
Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning
Yao Mu, Yuzheng Zhuang, Fei Ni +4
Adapting to the changes in transition dynamics is essential in robotic applications. By learning a conditional policy with a compact context, context-aware meta-reinforcement learn…
Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning
Zheng Wu, Yichen Xie, Wenzhao Lian +5
Humans are capable of abstracting various tasks as different combinations of multiple attributes. This perspective of compositionality is vital for human rapid learning and adaptio…
Performance-Driven Controller Tuning via Derivative-Free Reinforcement Learning
Yuheng Lei, Jianyu Chen, Shengbo Eben Li +1
Choosing an appropriate parameter set for the designed controller is critical for the final performance but usually requires a tedious and careful tuning process, which implies a s…
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation
Xiang Zhu, Shucheng Kang, Jianyu Chen
Reinforcement learning shows great potential to solve complex contact-rich robot manipulation tasks. However, the safety of using RL in the real world is a crucial problem, since u…
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
Yao Mu, Shoufa Chen, Mingyu Ding +3
Transformer has achieved great successes in learning vision and language representation, which is general across various downstream tasks. In visual control, learning transferable…
Reachability Constrained Reinforcement Learning
Dongjie Yu, Haitong Ma, Shengbo Eben Li +1
Constrained reinforcement learning (CRL) has gained significant interest recently, since safety constraints satisfaction is critical for real-world problems. However, existing CRL…
Flow-based Recurrent Belief State Learning for POMDPs
Xiaoyu Chen, Yao Mu, Ping Luo +2
Partially Observable Markov Decision Process (POMDP) provides a principled and generic framework to model real world sequential decision making processes but yet remains unsolved,…
Chance-Constrained Iterative Linear-Quadratic Stochastic Games
Hai Zhong, Yutaka Shimizu, Jianyu Chen
Dynamic game arises as a powerful paradigm for multi-robot planning, for which safety constraint satisfaction is crucial. Constrained stochastic games are of particular interest, a…
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +3
In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough d…
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +2
Safety is the major consideration in controlling complex dynamical systems using reinforcement learning (RL), where the safety certificate can provide provable safety guarantee. A…
Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp Sampling Loss
Xinghao Zhu, Yefan Zhou, Yongxiang Fan +3
Robotic grasping for a diverse set of objects is essential in many robot manipulation tasks. One promising approach is to learn deep grasping models from large training datasets of…
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
Baiyu Peng, Jingliang Duan, Jianyu Chen +6
Safety is essential for reinforcement learning (RL) applied in the real world. Adding chance constraints (or probabilistic constraints) is a suitable way to enhance RL safety under…
Constrained Iterative LQG for Real-Time Chance-Constrained Gaussian Belief Space Planning
Jianyu Chen, Yutaka Shimizu, Liting Sun +2
Motion planning under uncertainty is of significant importance for safety-critical systems such as autonomous vehicles. Such systems have to satisfy necessary constraints (e.g., co…
Feasible Actor-Critic: Constrained Reinforcement Learning for Ensuring Statewise Safety
Haitong Ma, Yang Guan, Shegnbo Eben Li +3
The safety constraints commonly used by existing safe reinforcement learning (RL) methods are defined only on expectation of initial states, but allow each certain state to be unsa…
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Haitong Ma, Jianyu Chen, Shengbo Eben Li +4
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks,…
Separated Proportional-Integral Lagrangian for Chance Constrained Reinforcement Learning
Baiyu Peng, Yao Mu, Jingliang Duan +3
Safety is essential for reinforcement learning (RL) applied in real-world tasks like autonomous driving. Chance constraints which guarantee the satisfaction of state constraints at…
Steadily Learn to Drive with Virtual Memory
Yuhang Zhang, Yao Mu, Yujie Yang +4
Reinforcement learning has shown great potential in developing high-level autonomous driving. However, for high-dimensional tasks, current RL methods suffer from low data efficienc…
Mixed Policy Gradient: off-policy reinforcement learning driven jointly by data and model
Yang Guan, Jingliang Duan, Shengbo Eben Li +3
Reinforcement learning (RL) shows great potential in sequential decision-making. At present, mainstream RL algorithms are data-driven, which usually yield better asymptotic perform…
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
Jinning Li, Liting Sun, Jianyu Chen +2
Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one hand, a single optimization/sampling-based mo…