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Found 13 papers, 2 papers with code
Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model
To remove the need for an oracle, we first transform the original robust MDPs into an alternative form, as the alternative form allows us to use stochastic gradient methods to solve the robust MDPs.
Statistical Estimation of Confounded Linear MDPs: An Instrumental Variable Approach
Specifically, we propose a two-stage estimator based on the instrumental variables and establish its statistical properties in the confounded MDPs with a linear structure.
KL-Entropy-Regularized RL with a Generative Model is Minimax Optimal
In this work, we consider and analyze the sample complexity of model-free reinforcement learning with a generative model.
Pluralistic Image Completion with Probabilistic Mixture-of-Experts
Second, the constraints for diversity are designed to be task-agnostic, which causes the constraints to not work well.
Federated Reinforcement Learning with Environment Heterogeneity
We study a Federated Reinforcement Learning (FedRL) problem in which $n$ agents collaboratively learn a single policy without sharing the trajectories they collected during agent-environment interaction.
AnomMAN: Detect Anomaly on Multi-view Attributed Networks
In fact, it remains a challenging task to consider all different kinds of interaction actions uniformly and detect anomalous instances in multi-view attributed networks.
A Statistical Analysis of Polyak-Ruppert Averaged Q-learning
We study Q-learning with Polyak-Ruppert averaging in a discounted Markov decision process in synchronous and tabular settings.
Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics
In this paper, we study the non-asymptotic and asymptotic performances of the optimal robust policy and value function of robust Markov Decision Processes(MDPs), where the optimal robust policy and value function are solved only from a generative model.
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
Regularized MDPs serve as a smooth version of original MDPs.
Communication-Efficient Local Decentralized SGD Methods
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication.
On the Convergence of FedAvg on Non-IID Data
In this paper, we analyze the convergence of \texttt{FedAvg} on non-iid data and establish a convergence rate of $\mathcal{O}(\frac{1}{T})$ for strongly convex and smooth problems, where $T$ is the number of SGDs.
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning
We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term.
Accelerated Value Iteration via Anderson Mixing
A2VI is more efficient than the modified policy iteration, which is a classical approximate method for policy evaluation.
