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Found 10 papers, 2 papers with code
Explaining Fast Improvement in Online Imitation Learning
Here policies are optimized by performing online learning on a sequence of loss functions that encourage the learner to mimic expert actions, and if the online learning has no regret, the agent can provably learn an expert-like policy.
Trajectory-wise Control Variates for Variance Reduction in Policy Gradient Methods
This can be attributed, at least in part, to the high variance in estimating the gradient of the task objective with Monte Carlo methods.
Predictor-Corrector Policy Optimization
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 leverages predictive models to accelerate policy learning.
Accelerating Imitation Learning with Predictive Models
When the model oracle is learned online, these algorithms can provably accelerate the best known convergence rate up to an order.
Fast Policy Learning through Imitation and Reinforcement
We show that if the switching time is properly randomized, LOKI can learn to outperform a suboptimal expert and converge faster than running policy gradient from scratch.
Manifold Regularization for Kernelized LSTD
Policy evaluation or value function or Q-function approximation is a key procedure in reinforcement learning (RL).
Imitation Learning for Agile Autonomous Driving
We present an end-to-end imitation learning system for agile, off-road autonomous driving using only low-cost on-board sensors.
Robotics
Prediction under Uncertainty in Sparse Spectrum Gaussian Processes with Applications to Filtering and Control
Sparse Spectrum Gaussian Processes (SSGPs) are a powerful tool for scaling Gaussian processes (GPs) to large datasets.
Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference
We benchmark our algorithms against several sampling-based and trajectory optimization-based motion planning algorithms on planning problems in multiple environments.
Robotics
Adaptive Probabilistic Trajectory Optimization via Efficient Approximate Inference
Robotic systems must be able to quickly and robustly make decisions when operating in uncertain and dynamic environments.
