Near-optimal offline reinforcement learning with linear representation: Leveraging variance information with pessimism
Offline reinforcement learning, which seeks to utilize offline/historical data to optimize
sequential decision-making strategies, has gained surging prominence in recent studies …
sequential decision-making strategies, has gained surging prominence in recent studies …
Nearly minimax optimal offline reinforcement learning with linear function approximation: Single-agent mdp and markov game
Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-
collected dataset without further interactions with the environment. While various algorithms …
collected dataset without further interactions with the environment. While various algorithms …
Posterior sampling with delayed feedback for reinforcement learning with linear function approximation
Recent studies in reinforcement learning (RL) have made significant progress by leveraging
function approximation to alleviate the sample complexity hurdle for better performance …
function approximation to alleviate the sample complexity hurdle for better performance …
Learn to match with no regret: Reinforcement learning in markov matching markets
We study a Markov matching market involving a planner and a set of strategic agents on the
two sides of the market. At each step, the agents are presented with a dynamical context …
two sides of the market. At each step, the agents are presented with a dynamical context …
Learning stochastic shortest path with linear function approximation
We study the stochastic shortest path (SSP) problem in reinforcement learning with linear
function approximation, where the transition kernel is represented as a linear mixture of …
function approximation, where the transition kernel is represented as a linear mixture of …
Pessimism in the face of confounders: Provably efficient offline reinforcement learning in partially observable markov decision processes
We study offline reinforcement learning (RL) in partially observable Markov decision
processes. In particular, we aim to learn an optimal policy from a dataset collected by a …
processes. In particular, we aim to learn an optimal policy from a dataset collected by a …
Provable benefit of multitask representation learning in reinforcement learning
As representation learning becomes a powerful technique to reduce sample complexity in
reinforcement learning (RL) in practice, theoretical understanding of its advantage is still …
reinforcement learning (RL) in practice, theoretical understanding of its advantage is still …
Noise-adaptive thompson sampling for linear contextual bandits
Linear contextual bandits represent a fundamental class of models with numerous real-
world applications, and it is critical to develop algorithms that can effectively manage noise …
world applications, and it is critical to develop algorithms that can effectively manage noise …
Sample complexity of nonparametric off-policy evaluation on low-dimensional manifolds using deep networks
We consider the off-policy evaluation problem of reinforcement learning using deep
convolutional neural networks. We analyze the deep fitted Q-evaluation method for …
convolutional neural networks. We analyze the deep fitted Q-evaluation method for …
Cascaded gaps: Towards logarithmic regret for risk-sensitive reinforcement learning
In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement
learning based on the entropic risk measure. We propose a novel definition of sub-optimality …
learning based on the entropic risk measure. We propose a novel definition of sub-optimality …