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Found 13 papers, 3 papers with code
Toward $L_\infty$-recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields
Our key technical novelty is to prove that the degree-$k$ spherical harmonics components of a function from Gaussian random field cannot be spiky in that their $L_\infty$/$L_2$ ratios are upperbounded by $O(d \sqrt{\ln k})$ with high probability.
Model-based Offline Reinforcement Learning with Local Misspecification
We present a model-based offline reinforcement learning policy performance lower bound that explicitly captures dynamics model misspecification and distribution mismatch and we propose an empirical algorithm for optimal offline policy selection.
First Steps Toward Understanding the Extrapolation of Nonlinear Models to Unseen Domains
The question is very challenging because even two-layer neural networks cannot be guaranteed to extrapolate outside the support of the training distribution without further assumptions on the domain shift.
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making
Past research on interactive decision making problems (bandits, reinforcement learning, etc.)
Design of Experiments for Stochastic Contextual Linear Bandits
In the stochastic linear contextual bandit setting there exist several minimax procedures for exploration with policies that are reactive to the data being acquired.
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature
This paper studies model-based bandit and reinforcement learning (RL) with nonlinear function approximations.
Refined Analysis of FPL for Adversarial Markov Decision Processes
We consider the adversarial Markov Decision Process (MDP) problem, where the rewards for the MDP can be adversarially chosen, and the transition function can be either known or unknown.
Multinomial Logit Bandit with Low Switching Cost
We also present the ESUCB algorithm with item switching cost $O(N \log^2 T)$.
On the Expressivity of Neural Networks for Deep Reinforcement Learning
We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics.
Bootstrapping the Expressivity with Model-based Planning
We compare the model-free reinforcement learning with the model-based approaches through the lens of the expressive power of neural networks for policies, $Q$-functions, and dynamics.
$\sqrt{n}$-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank
Our learning algorithm, Adaptive Value-function Elimination (AVE), is inspired by the policy elimination algorithm proposed in (Jiang et al., 2017), known as OLIVE.
Exploration via Hindsight Goal Generation
Goal-oriented reinforcement learning has recently been a practical framework for robotic manipulation tasks, in which an agent is required to reach a certain goal defined by a function on the state space.
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP
A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient.
