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Benchmarking Batch Deep Reinforcement Learning Algorithms
Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh +1
Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment. Following…
Adaptive Sampling for Estimating Multiple Probability Distributions
Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh
We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms of four common distance measures: ,…
Multi-step Greedy Reinforcement Learning Algorithms
Manan Tomar, Yonathan Efroni, Mohammad Ghavamzadeh
Multi-step greedy policies have been extensively used in model-based reinforcement learning (RL), both when a model of the environment is available (e.g.,~in the game of Go) and wh…
Online Planning with Lookahead Policies
Yonathan Efroni, Mohammad Ghavamzadeh, Shie Mannor
Real Time Dynamic Programming (RTDP) is an online algorithm based on Dynamic Programming (DP) that acts by 1-step greedy planning. Unlike DP, RTDP does not require access to the en…
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control
Nir Levine, Yinlam Chow, Rui Shu +3
Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed t…
Randomized Exploration in Generalized Linear Bandits
Branislav Kveton, Manzil Zaheer, Csaba Szepesvari +3
We study two randomized algorithms for generalized linear bandits. The first, GLM-TSL, samples a generalized linear model (GLM) from the Laplace approximation to the posterior dist…
Active Learning for Binary Classification with Abstention
Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi
We construct and analyze active learning algorithms for the problem of binary classification with abstention. We consider three abstention settings: \emph{fixed-cost} and two varia…
Binary Classification with Bounded Abstention Rate
Shubhanshu Shekhar, Mohammad Ghavamzadeh, Tara Javidi
We consider the problem of binary classification with abstention in the relatively less studied \emph{bounded-rate} setting. We begin by obtaining a characterization of the Bayes o…
Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies
Yonathan Efroni, Nadav Merlis, Mohammad Ghavamzadeh +1
State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms typically act by iteratively solving empirical models, i.e., by performing \emph{full-planning} on Mar…
Perturbed-History Exploration in Stochastic Linear Bandits
Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh +1
We propose a new online algorithm for cumulative regret minimization in a stochastic linear bandit. The algorithm pulls the arm with the highest estimated reward in a linear model…
Lyapunov-based Safe Policy Optimization for Continuous Control
Yinlam Chow, Ofir Nachum, Aleksandra Faust +2
We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through safe policies, i.e.,~policies that do n…
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh +1
We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds i.i.d. pseudo-rewards to its history in round an…