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Found 24 papers, 0 papers with code
Settling Constant Regrets in Linear Markov Decision Processes
To the best of our knowledge, Cert-LSVI-UCB is the first algorithm to achieve a constant, instance-dependent, high-probability regret bound in RL with linear function approximation for infinite runs without relying on prior distribution assumptions.
Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback
Learning from human feedback plays an important role in aligning generative models, such as large language models (LLM).
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
Our algorithm achieves an $\tilde{\mathcal O}(dB_*\sqrt{K})$ regret bound, where $d$ is the dimension of the feature mapping in the linear transition kernel, $B_*$ is the upper bound of the total cumulative cost for the optimal policy, and $K$ is the number of episodes.
Towards Robust Model-Based Reinforcement Learning Against Adversarial Corruption
We also prove a lower bound to show that the additive dependence on $C$ is optimal.
Model-based Reinforcement Learning
reinforcement-learning
+1
Reinforcement Learning from Human Feedback with Active Queries
Aligning large language models (LLM) with human preference plays a key role in building modern generative models and can be achieved by reinforcement learning from human feedback (RLHF).
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
The exploration-exploitation dilemma has been a central challenge in reinforcement learning (RL) with complex model classes.
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
However, limited works on offline RL with non-linear function approximation have instance-dependent regret guarantees.
Horizon-free Reinforcement Learning in Adversarial Linear Mixture MDPs
Recent studies have shown that episodic reinforcement learning (RL) is no harder than bandits when the total reward is bounded by $1$, and proved regret bounds that have a polylogarithmic dependence on the planning horizon $H$.
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension
Recently, there has been remarkable progress in reinforcement learning (RL) with general function approximation.
Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximation
We study multi-agent reinforcement learning in the setting of episodic Markov decision processes, where multiple agents cooperate via communication through a central server.
On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits
We show that, when the misspecification level $\zeta$ is dominated by $\tilde O (\Delta / \sqrt{d})$ with $\Delta$ being the minimal sub-optimality gap and $d$ being the dimension of the contextual vectors, our algorithm enjoys the same gap-dependent regret bound $\tilde O (d^2/\Delta)$ as in the well-specified setting up to logarithmic factors.
Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency
We propose a variance-adaptive algorithm for linear mixture MDPs, which achieves a problem-dependent horizon-free regret bound that can gracefully reduce to a nearly constant regret for deterministic MDPs.
Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processes
We study reinforcement learning (RL) with linear function approximation.
A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits
To the best of our knowledge, this is the first provably efficient algorithm that allows fully asynchronous communication for federated contextual linear bandits, while achieving the same regret guarantee as in the single-agent setting.
Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions
We show that for both known $C$ and unknown $C$ cases, our algorithm with proper choice of hyperparameter achieves a regret that nearly matches the lower bounds.
Optimal Online Generalized Linear Regression with Stochastic Noise and Its Application to Heteroscedastic Bandits
We study the problem of online generalized linear regression in the stochastic setting, where the label is generated from a generalized linear model with possibly unbounded additive noise.
Learning Stochastic Shortest Path with Linear Function Approximation
To the best of our knowledge, this is the first algorithm with a sublinear regret guarantee for learning linear mixture SSP.
Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes
To the best of our knowledge, this is the first provable privacy-preserving RL algorithm with linear function approximation.
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation
The uniform-PAC guarantee is the strongest possible guarantee for reinforcement learning in the literature, which can directly imply both PAC and high probability regret bounds, making our algorithm superior to all existing algorithms with linear function approximation.
Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
For the offline counterpart, ReLEX-LCB, we show that the algorithm can find the optimal policy if the representation class can cover the state-action space and achieves gap-dependent sample complexity.
Near-optimal Policy Optimization Algorithms for Learning Adversarial Linear Mixture MDPs
In this paper, we study RL in episodic MDPs with adversarial reward and full information feedback, where the unknown transition probability function is a linear function of a given feature mapping, and the reward function can change arbitrarily episode by episode.
Logarithmic Regret for Reinforcement Learning with Linear Function Approximation
Reinforcement learning (RL) with linear function approximation has received increasing attention recently.
Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs
We study the reinforcement learning problem for discounted Markov Decision Processes (MDPs) under the tabular setting.
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping
We propose a novel algorithm that makes use of the feature mapping and obtains a $\tilde O(d\sqrt{T}/(1-\gamma)^2)$ regret, where $d$ is the dimension of the feature space, $T$ is the time horizon and $\gamma$ is the discount factor of the MDP.
