130 research outputs found
Procrastinated Tree Search: Black-box Optimization with Delayed, Noisy, and Multi-fidelity Feedback
In black-box optimization problems, we aim to maximize an unknown objective
function, where the function is only accessible through feedbacks of an
evaluation or simulation oracle. In real-life, the feedbacks of such oracles
are often noisy and available after some unknown delay that may depend on the
computation time of the oracle. Additionally, if the exact evaluations are
expensive but coarse approximations are available at a lower cost, the
feedbacks can have multi-fidelity. In order to address this problem, we propose
a generic extension of hierarchical optimistic tree search (HOO), called
ProCrastinated Tree Search (PCTS), that flexibly accommodates a delay and
noise-tolerant bandit algorithm. We provide a generic proof technique to
quantify regret of PCTS under delayed, noisy, and multi-fidelity feedbacks.
Specifically, we derive regret bounds of PCTS enabled with delayed-UCB1 (DUCB1)
and delayed-UCB-V (DUCBV) algorithms. Given a horizon , PCTS retains the
regret bound of non-delayed HOO for expected delay of and worsens
by for expected delays of for
. We experimentally validate on multiple synthetic functions
and hyperparameter tuning problems that PCTS outperforms the state-of-the-art
black-box optimization methods for feedbacks with different noise levels,
delays, and fidelity
Research on Development and Application of Low-Voltage and High-Speed Power Line Communication Technology
Low-voltage and high-speed power line communication (PLC) technology, as the main means of communication construction, enables the masses to obtain higher quality services and has attracted more and more public attention. This paper is divided into four parts: the introduction of PLC technology, the application significance of low-voltage and high-speed PLC communication technology, the characteristics of PLC channel and the application and comparison of high-speed PLC technology
Composite finiteâtime convergent guidance law for maneuvering targets with secondâorder autopilot lag
This paper aims to develop a new finiteâtime convergent guidance law for intercepting maneuvering targets accounting for secondâorder autopilot lag. The guidance law is applied to guarantee that the line of sight (LOS) angular rate converges to zero in finite time and results in a direct interception. The effect of autopilot dynamics can be compensated based on the finiteâtime backstepping control method. The time derivative of the virtual input is avoided, taking advantage of integralâtype Lyapunov functions. A finiteâtime disturbance observer (FTDOB) is used to estimate the lumped uncertainties and highâorder derivatives to improve the robustness and accuracy of the guidance system. Finiteâtime stability for the closedâloop guidance system is analyzed using the Lyapunov function. Simulation results and comparisons are presented to illustrate the effectiveness of the guidance strategy
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
In this paper, we present \textsc{JoinGym}, an efficient and lightweight
query optimization environment for reinforcement learning (RL). Join order
selection (JOS) is a classic NP-hard combinatorial optimization problem from
database query optimization and can serve as a practical testbed for the
generalization capabilities of RL algorithms. We describe how to formulate each
of the left-deep and bushy variants of the JOS problem as a Markov Decision
Process (MDP), and we provide an implementation adhering to the standard
Gymnasium API. We highlight that our implementation \textsc{JoinGym} is
completely based on offline traces of all possible joins, which enables RL
practitioners to easily and quickly test their methods on a realistic data
management problem without needing to setup any systems. Moreover, we also
provide all possible join traces on novel SQL queries generated from the
IMDB dataset. Upon benchmarking popular RL algorithms, we find that at least
one method can obtain near-optimal performance on train-set queries but their
performance degrades by several orders of magnitude on test-set queries. This
gap motivates further research for RL algorithms that generalize well in
multi-task combinatorial optimization problems.Comment: We will make all the queries available soo
SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning
SkinnerDB is designed from the ground up for reliable join ordering. It
maintains no data statistics and uses no cost or cardinality models. Instead,
it uses reinforcement learning to learn optimal join orders on the fly, during
the execution of the current query. To that purpose, we divide the execution of
a query into many small time slices. Different join orders are tried in
different time slices. We merge result tuples generated according to different
join orders until a complete result is obtained. By measuring execution
progress per time slice, we identify promising join orders as execution
proceeds.
Along with SkinnerDB, we introduce a new quality criterion for query
execution strategies. We compare expected execution cost against execution cost
for an optimal join order. SkinnerDB features multiple execution strategies
that are optimized for that criterion. Some of them can be executed on top of
existing database systems. For maximal performance, we introduce a customized
execution engine, facilitating fast join order switching via specialized
multi-way join algorithms and tuple representations.
We experimentally compare SkinnerDB's performance against various baselines,
including MonetDB, Postgres, and adaptive processing methods. We consider
various benchmarks, including the join order benchmark and TPC-H variants with
user-defined functions. Overall, the overheads of reliable join ordering are
negligible compared to the performance impact of the occasional, catastrophic
join order choice
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