233 citations · 319 across the 9 of their papers we have counts for
10 papers
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods
Ching-An Cheng, Xinyan Yan, Byron Boots
Policy gradient methods have demonstrated success in reinforcement learning tasks that have high-dimensional continuous state and action spaces. However, policy gradient methods ar…
Predictor-Corrector Policy Optimization
Ching-An Cheng, Xinyan Yan, Nathan Ratliff +1
We present a predictor-corrector framework, called PicCoLO, that can transform a first-order model-free reinforcement or imitation learning algorithm into a new hybrid method that…
Accelerating Imitation Learning with Predictive Models
Ching-An Cheng, Xinyan Yan, Evangelos A. Theodorou +1
Sample efficiency is critical in solving real-world reinforcement learning problems, where agent-environment interactions can be costly. Imitation learning from expert advice has p…
Fast Policy Learning through Imitation and Reinforcement
Ching-An Cheng, Xinyan Yan, Nolan Wagener +1
Imitation learning (IL) consists of a set of tools that leverage expert demonstrations to quickly learn policies. However, if the expert is suboptimal, IL can yield policies with i…
Manifold Regularization for Kernelized LSTD
Xinyan Yan, Krzysztof Choromanski, Byron Boots +1
Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL). It is a necessary component of policy iteration and can be used f…
Agile Autonomous Driving using End-to-End Deep Imitation Learning
Yunpeng Pan, Ching-An Cheng, Kamil Saigol +4
We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost sensors. By imitating a model predictive controller equipped with adva…