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Found 13 papers, 1 papers with code
ASID: Active Exploration for System Identification in Robotic Manipulation
In this work, we propose a learning system that can leverage a small amount of real-world data to autonomously refine a simulation model and then plan an accurate control strategy that can be deployed in the real world.
Fair Active Learning in Low-Data Regimes
In this work, we address the challenges of reducing bias and improving accuracy in data-scarce environments, where the cost of collecting labeled data prohibits the use of large, labeled datasets.
Instance-Optimality in Interactive Decision Making: Toward a Non-Asymptotic Theory
We consider the development of adaptive, instance-dependent algorithms for interactive decision making (bandits, reinforcement learning, and beyond) that, rather than only performing well in the worst case, adapt to favorable properties of real-world instances for improved performance.
Leveraging Offline Data in Online Reinforcement Learning
Practical scenarios often motivate an intermediate setting: if we have some set of offline data and, in addition, may also interact with the environment, how can we best use the offline data to minimize the number of online interactions necessary to learn an $\epsilon$-optimal policy?
Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design
While much progress has been made in understanding the minimax sample complexity of reinforcement learning (RL) -- the complexity of learning on the "worst-case" instance -- such measures of complexity often do not capture the true difficulty of learning.
Active Learning with Safety Constraints
To our knowledge, our results are the first on best-arm identification in linear bandits with safety constraints.
Reward-Free RL is No Harder Than Reward-Aware RL in Linear Markov Decision Processes
We first develop a computationally efficient algorithm for reward-free RL in a $d$-dimensional linear MDP with sample complexity scaling as $\widetilde{\mathcal{O}}(d^2 H^5/\epsilon^2)$.
First-Order Regret in Reinforcement Learning with Linear Function Approximation: A Robust Estimation Approach
Obtaining first-order regret bounds -- regret bounds scaling not as the worst-case but with some measure of the performance of the optimal policy on a given instance -- is a core question in sequential decision-making.
Best Arm Identification with Safety Constraints
The best arm identification problem in the multi-armed bandit setting is an excellent model of many real-world decision-making problems, yet it fails to capture the fact that in the real-world, safety constraints often must be met while learning.
Beyond No Regret: Instance-Dependent PAC Reinforcement Learning
We show this is not possible -- there exists a fundamental tradeoff between achieving low regret and identifying an $\epsilon$-optimal policy at the instance-optimal rate.
Task-Optimal Exploration in Linear Dynamical Systems
Along the way, we establish that certainty equivalence decision making is instance- and task-optimal, and obtain the first algorithm for the linear quadratic regulator problem which is instance-optimal.
Experimental Design for Regret Minimization in Linear Bandits
In this paper we propose a novel experimental design-based algorithm to minimize regret in online stochastic linear and combinatorial bandits.
Active Learning for Identification of Linear Dynamical Systems
We propose an algorithm to actively estimate the parameters of a linear dynamical system.
