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Found 38 papers, 4 papers with code
Finite-Time Error Analysis of Soft Q-Learning: Switching System Approach
We hope that our analysis will deepen the current understanding of soft Q-learning by establishing connections with switching system models and may even pave the way for new frameworks in the finite-time analysis of other reinforcement learning algorithms.
Analysis of Off-Policy Multi-Step TD-Learning with Linear Function Approximation
This paper analyzes multi-step TD-learning algorithms within the `deadly triad' scenario, characterized by linear function approximation, off-policy learning, and bootstrapping.
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model
Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches.
Harnessing Membership Function Dynamics for Stability Analysis of T-S Fuzzy Systems
The main goal of this paper is to develop a new linear matrix inequality (LMI) condition for the asymptotic stability of continuous-time Takagi-Sugeno (T-S) fuzzy systems.
A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
However, with a constant gradient descent step size, this spike only carries information from the linear component of the target function and therefore learning non-linear components is impossible.
Suppressing Overestimation in Q-Learning through Adversarial Behaviors
The goal of this paper is to propose a new Q-learning algorithm with a dummy adversarial player, which is called dummy adversarial Q-learning (DAQ), that can effectively regulate the overestimation bias in standard Q-learning.
A primal-dual perspective for distributed TD-learning
The goal of this paper is to investigate distributed temporal difference (TD) learning for a networked multi-agent Markov decision process.
On the Local Quadratic Stability of T-S Fuzzy Systems in the Vicinity of the Origin
Moreover, we establish that the proposed methods offer necessary and sufficient conditions for the local exponential stability of T-S fuzzy systems.
Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes
The main goal of this paper is to investigate continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs).
Temporal Difference Learning with Experience Replay
Temporal-difference (TD) learning is widely regarded as one of the most popular algorithms in reinforcement learning (RL).
Finite-Time Analysis of Minimax Q-Learning for Two-Player Zero-Sum Markov Games: Switching System Approach
The objective of this paper is to investigate the finite-time analysis of a Q-learning algorithm applied to two-player zero-sum Markov games.
Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits
MOLAR improves the dependence of the estimation error on the data dimension, compared to independent least squares estimates.
TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering
We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses.
Backstepping Temporal Difference Learning
Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications.
Demystifying Disagreement-on-the-Line in High Dimensions
Evaluating the performance of machine learning models under distribution shift is challenging, especially when we only have unlabeled data from the shifted (target) domain, along with labeled data from the original (source) domain.
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View
Q-learning has long been one of the most popular reinforcement learning algorithms, and theoretical analysis of Q-learning has been an active research topic for decades.
Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
In modern machine learning, users often have to collaborate to learn the distribution of the data.
Investigating the Role of Image Retrieval for Visual Localization -- An exhaustive benchmark
In order to investigate the consequences for visual localization, this paper focuses on understanding the role of image retrieval for multiple visual localization paradigms.
Finite-Time Analysis of Temporal Difference Learning: Discrete-Time Linear System Perspective
TD-learning is a fundamental algorithm in the field of reinforcement learning (RL), that is employed to evaluate a given policy by estimating the corresponding value function for a Markov decision process.
A Single Correspondence Is Enough: Robust Global Registration to Avoid Degeneracy in Urban Environments
As verified in indoor and outdoor 3D LiDAR datasets, our proposed method yields robust global registration performance compared with other global registration methods, even for distant point cloud pairs.
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Given these metric poses and monocular sequences, we propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation.
T-Cal: An optimal test for the calibration of predictive models
We find that detecting mis-calibration is only possible when the conditional probabilities of the classes are sufficiently smooth functions of the predictions.
Regularized Q-learning
This paper develops a new Q-learning algorithm that converges when linear function approximation is used.
Control Theoretic Analysis of Temporal Difference Learning
The goal of this manuscript is to conduct a controltheoretic analysis of Temporal Difference (TD) learning algorithms.
New Versions of Gradient Temporal Difference Learning
Sutton, Szepesv\'{a}ri and Maei introduced the first gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training.
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes
We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e. g., a department store or a metro station.
Large-scale Localization Datasets in Crowded Indoor Spaces
In this paper, we introduce 5 new indoor datasets for visual localization in challenging real-world environments.
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings.
A Discrete-Time Switching System Analysis of Q-learning
Based on these two systems, we derive a new finite-time error bound of asynchronous Q-learning when a constant stepsize is used.
A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms
This paper develops a novel and unified framework to analyze the convergence of a large family of Q-learning algorithms from the switching system perspective.
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
We present a novel algorithm for self-supervised monocular depth completion.
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
In this paper, we propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss.
Periodic Q-Learning
The use of target networks is a common practice in deep reinforcement learning for stabilizing the training; however, theoretical understanding of this technique is still limited.
A Unified Switching System Perspective and O.D.E. Analysis of Q-Learning Algorithms
In this paper, we introduce a unified framework for analyzing a large family of Q-learning algorithms, based on switching system perspectives and ODE-based stochastic approximation.
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents
This article reviews recent advances in multi-agent reinforcement learning algorithms for large-scale control systems and communication networks, which learn to communicate and cooperate.
Distributed Optimization
Multi-agent Reinforcement Learning
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Target-Based Temporal Difference Learning
The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side.
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
The encoding is useful for sharing local visual observations with other agents under communication resource constraints.
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and action sets.
