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Found 9 papers, 1 papers with code
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
We study reward-free reinforcement learning (RL) under general non-linear function approximation, and establish sample efficiency and hardness results under various standard structural assumptions.
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems
Learning-based control of linear systems received a lot of attentions recently.
Joint Learning of Linear Time-Invariant Dynamical Systems
Linear time-invariant systems are very popular models in system theory and applications.
Model-free Representation Learning and Exploration in Low-rank MDPs
In this work, we present the first model-free representation learning algorithms for low rank MDPs.
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
We analyze the theoretical properties of the proposed algorithm, providing optimality guarantees and demonstrate our approach on simulated environments and a real clinical task.
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
As an extension, we also consider the more challenging problem of model selection, where the state features are unknown and can be chosen from a large candidate set.
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations
We address the opportunity to maximize the utility of an overall computing system by employing reinforcement learning to guide the configuration of the set of interacting modules that comprise the system.
No-regret Exploration in Contextual Reinforcement Learning
We consider the recently proposed reinforcement learning (RL) framework of Contextual Markov Decision Processes (CMDP), where the agent interacts with a (potentially adversarial) sequence of episodic tabular MDPs.
Markov Decision Processes with Continuous Side Information
Because our lower bound has an exponential dependence on the dimension, we consider a tractable linear setting where the context is used to create linear combinations of a finite set of MDPs.
