Deep reinforcement learning: An overview
Y Li - arXiv preprint arXiv:1701.07274, 2017 - arxiv.org
We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …
We discuss six core elements, six important mechanisms, and twelve applications. We start …
Evaluation of deep learning models for multi-step ahead time series prediction
R Chandra, S Goyal, R Gupta - Ieee Access, 2021 - ieeexplore.ieee.org
Time series prediction with neural networks has been the focus of much research in the past
few decades. Given the recent deep learning revolution, there has been much attention in …
few decades. Given the recent deep learning revolution, there has been much attention in …
Deep reinforcement learning
SE Li - Reinforcement learning for sequential decision and …, 2023 - Springer
Similar to humans, RL agents use interactive learning to successfully obtain satisfactory
decision strategies. However, in many cases, it is desirable to learn directly from …
decision strategies. However, in many cases, it is desirable to learn directly from …
Kernel instrumental variable regression
Instrumental variable (IV) regression is a strategy for learning causal relationships in
observational data. If measurements of input X and output Y are confounded, the causal …
observational data. If measurements of input X and output Y are confounded, the causal …
Query-based workload forecasting for self-driving database management systems
The first step towards an autonomous database management system (DBMS) is the ability to
model the target application's workload. This is necessary to allow the system to anticipate …
model the target application's workload. This is necessary to allow the system to anticipate …
Provably efficient reinforcement learning in partially observable dynamical systems
Abstract We study Reinforcement Learning for partially observable systems using function
approximation. We propose a new PO-bilinear framework, that is general enough to include …
approximation. We propose a new PO-bilinear framework, that is general enough to include …
Pac reinforcement learning for predictive state representations
In this paper we study online Reinforcement Learning (RL) in partially observable dynamical
systems. We focus on the Predictive State Representations (PSRs) model, which is an …
systems. We focus on the Predictive State Representations (PSRs) model, which is an …
Future-dependent value-based off-policy evaluation in pomdps
We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general
function approximation. Existing methods such as sequential importance sampling …
function approximation. Existing methods such as sequential importance sampling …
Recurrent neural filters: Learning independent bayesian filtering steps for time series prediction
Despite the recent popularity of deep generative state space models, few comparisons have
been made between network architectures and the inference steps of the Bayesian filtering …
been made between network architectures and the inference steps of the Bayesian filtering …
Energy-based predictive representations for partially observed reinforcement learning
In real-world applications, handling partial observability is a common requirement for
reinforcement learning algorithms, which is not captured by a Markov decision process …
reinforcement learning algorithms, which is not captured by a Markov decision process …