Search Results for author: Jonathan Lee

Found 13 papers, 1 papers with code

An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction

no code implementations journal 2022 TING-JEN CHANG, SAMANTHA LEE, Jonathan Lee, AND CHI-JIE LU

In this study, an interval-valued time series forecasting scheme based on probability distribution information features of interval-valued data with machine learning algorithms is proposed to enhance electric power generation forecasting.

Descriptive regression +2

Dueling RL: Reinforcement Learning with Trajectory Preferences

no code implementations8 Nov 2021 Aldo Pacchiano, Aadirupa Saha, Jonathan Lee

We consider the problem of preference based reinforcement learning (PbRL), where, unlike traditional reinforcement learning, an agent receives feedback only in terms of a 1 bit (0/1) preference over a trajectory pair instead of absolute rewards for them.

reinforcement-learning Reinforcement Learning (RL)

Design of Experiments for Stochastic Contextual Linear Bandits

no code implementations NeurIPS 2021 Andrea Zanette, Kefan Dong, Jonathan Lee, Emma Brunskill

In the stochastic linear contextual bandit setting there exist several minimax procedures for exploration with policies that are reactive to the data being acquired.

Near Optimal Policy Optimization via REPS

no code implementations NeurIPS 2021 Aldo Pacchiano, Jonathan Lee, Peter Bartlett, Ofir Nachum

Since its introduction a decade ago, \emph{relative entropy policy search} (REPS) has demonstrated successful policy learning on a number of simulated and real-world robotic domains, not to mention providing algorithmic components used by many recently proposed reinforcement learning (RL) algorithms.

Reinforcement Learning (RL)

Is Q-Learning Provably Efficient? An Extended Analysis

no code implementations22 Sep 2020 Kushagra Rastogi, Jonathan Lee, Fabrice Harel-Canada, Aditya Joglekar

This work extends the analysis of the theoretical results presented within the paper Is Q-Learning Provably Efficient?

Q-Learning reinforcement-learning +1

Continuous Online Learning and New Insights to Online Imitation Learning

no code implementations3 Dec 2019 Jonathan Lee, Ching-An Cheng, Ken Goldberg, Byron Boots

We prove that there is a fundamental equivalence between achieving sublinear dynamic regret in COL and solving certain EPs, and we present a reduction from dynamic regret to both static regret and convergence rate of the associated EP.

Imitation Learning

Online Learning with Continuous Variations: Dynamic Regret and Reductions

no code implementations19 Feb 2019 Ching-An Cheng, Jonathan Lee, Ken Goldberg, Byron Boots

Furthermore, we show for COL a reduction from dynamic regret to both static regret and convergence in the associated EP, allowing us to analyze the dynamic regret of many existing algorithms.

DART: Noise Injection for Robust Imitation Learning

2 code implementations27 Mar 2017 Michael Laskey, Jonathan Lee, Roy Fox, Anca Dragan, Ken Goldberg

One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy.

Imitation Learning

Comparing Human-Centric and Robot-Centric Sampling for Robot Deep Learning from Demonstrations

no code implementations4 Oct 2016 Michael Laskey, Caleb Chuck, Jonathan Lee, Jeffrey Mahler, Sanjay Krishnan, Kevin Jamieson, Anca Dragan, Ken Goldberg

Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error.

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