Search Results for author: Letian Chen

Found 10 papers, 4 papers with code

Learning Models of Adversarial Agent Behavior under Partial Observability

1 code implementation19 Jun 2023 Sean Ye, Manisha Natarajan, Zixuan Wu, Rohan Paleja, Letian Chen, Matthew C. Gombolay

The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction.

Safe Inverse Reinforcement Learning via Control Barrier Function

no code implementations6 Dec 2022 Yue Yang, Letian Chen, Matthew Gombolay

Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the desired skill and for the robot to efficiently learn from the associated data than for a human to engineer a reward function for the robot to learn the skill via reinforcement learning (RL).

reinforcement-learning Reinforcement Learning (RL)

Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations

no code implementations24 Sep 2022 Letian Chen, Sravan Jayanthi, Rohan Paleja, Daniel Martin, Viacheslav Zakharov, Matthew Gombolay

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.

Continuous Control reinforcement-learning +1

Strategy Discovery and Mixture in Lifelong Learning from Heterogeneous Demonstration

no code implementations14 Feb 2022 Sravan Jayanthi, Letian Chen, Matthew Gombolay

Learning from Demonstration (LfD) approaches empower end-users to teach robots novel tasks via demonstrations of the desired behaviors, democratizing access to robotics.

Continuous Control

Towards Sample-efficient Apprenticeship Learning from Suboptimal Demonstration

no code implementations8 Oct 2021 Letian Chen, Rohan Paleja, Matthew Gombolay

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform novel tasks by providing demonstrations.

Learning from Suboptimal Demonstration via Self-Supervised Reward Regression

1 code implementation17 Oct 2020 Letian Chen, Rohan Paleja, Matthew Gombolay

Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration.

regression

Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations

1 code implementation NeurIPS 2020 Rohan Paleja, Andrew Silva, Letian Chen, Matthew Gombolay

Resource scheduling and coordination is an NP-hard optimization requiring an efficient allocation of agents to a set of tasks with upper- and lower bound temporal and resource constraints.

Decision Making Scheduling

Efficient Model-Free Reinforcement Learning Using Gaussian Process

no code implementations11 Dec 2018 Ying Fan, Letian Chen, Yizhou Wang

Efficient Reinforcement Learning usually takes advantage of demonstration or good exploration strategy.

reinforcement-learning Reinforcement Learning (RL)

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