Search Results for author:
Found 16 papers, 6 papers with code
Accelerated Value Iteration via Anderson Mixing
A2VI is more efficient than the modified policy iteration, which is a classical approximate method for policy evaluation.
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.
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.
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.
Finding the Near Optimal Policy via Adaptive Reduced Regularization in MDPs
Regularized MDPs serve as a smooth version of original MDPs.
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.
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.
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.
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.
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.
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.
Robust Markov Decision Processes without Model Estimation
Moreover, we prove the alternative form still plays a similar role as the original form.
Semi-Infinitely Constrained Markov Decision Processes and Efficient Reinforcement Learning
To the best of our knowledge, we are the first to apply tools from semi-infinitely programming (SIP) to solve constrained reinforcement learning problems.
Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guarantees
Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named $\text{BOGD}_\text{IP}$, and establish an $\mathcal{O}(T^{3/4}(1+P_T))$ dynamic regret bound, where $P_T$ denotes the path-length of the comparator sequence.
Regularization and Variance-Weighted Regression Achieves Minimax Optimality in Linear MDPs: Theory and Practice
Mirror descent value iteration (MDVI), an abstraction of Kullback-Leibler (KL) and entropy-regularized reinforcement learning (RL), has served as the basis for recent high-performing practical RL algorithms.
Estimation and Inference in Distributional Reinforcement Learning
This implies the distributional policy evaluation problem can be solved with sample efficiency.
Distributional Reinforcement Learning
reinforcement-learning
