234 research outputs found
Creation of two-level liquid cooling system of PC
This paper devoted to creation of two-level liquid cooling system of PC. We found the solution to change a traditional air cooling system for the liquid cooling system with ethylene glycol as a coolant. The cooling system managed to reduce the temperature by up to 4-10°C while down time and ~11°C while load time
Katakomba: Tools and Benchmarks for Data-Driven NetHack
NetHack is known as the frontier of reinforcement learning research where
learning-based methods still need to catch up to rule-based solutions. One of
the promising directions for a breakthrough is using pre-collected datasets
similar to recent developments in robotics, recommender systems, and more under
the umbrella of offline reinforcement learning (ORL). Recently, a large-scale
NetHack dataset was released; while it was a necessary step forward, it has yet
to gain wide adoption in the ORL community. In this work, we argue that there
are three major obstacles for adoption: resource-wise, implementation-wise, and
benchmark-wise. To address them, we develop an open-source library that
provides workflow fundamentals familiar to the ORL community: pre-defined
D4RL-style tasks, uncluttered baseline implementations, and reliable evaluation
tools with accompanying configs and logs synced to the cloud.Comment: Neural Information Processing Systems (NeurIPS 2023) Track on
Datasets and Benchmarks. Source code at
https://github.com/corl-team/katakomb
Estimating the scale of stone axe production: A case study from Onega Lake, Russian Karelia
The industry of metatuff axes and adzes on the western coast of Onega Lake (Eneolithic period, ca. 3500 – 1500 cal. BC) allows assuming some sort of craft specialization. Excavations of a workshop site Fofanovo XIII, conducted in 2010-2011, provided an extremely large assemblage of artefacts (over 350000 finds from just 30 m2, mostly production debitage). An attempt to estimate the output of production within the excavated area is based on experimental data from a series of replication experiments. Mass-analysis with the aid of image recognition software was used to obtain raw data from flakes from excavations and experiments. Statistical evaluation assures that the experimental results can be used as a basement for calculations. According to the proposed estimation, some 500 – 1000 tools could have been produced here, and this can be qualified as an evidence of “mass-production”
CORL: Research-oriented Deep Offline Reinforcement Learning Library
CORL is an open-source library that provides thoroughly benchmarked
single-file implementations of both deep offline and offline-to-online
reinforcement learning algorithms. It emphasizes a simple developing experience
with a straightforward codebase and a modern analysis tracking tool. In CORL,
we isolate methods implementation into separate single files, making
performance-relevant details easier to recognize. Additionally, an experiment
tracking feature is available to help log metrics, hyperparameters,
dependencies, and more to the cloud. Finally, we have ensured the reliability
of the implementations by benchmarking commonly employed D4RL datasets
providing a transparent source of results that can be reused for robust
evaluation tools such as performance profiles, probability of improvement, or
expected online performance.Comment: Conference on Neural Information Processing Systems (NeurIPS 2023)
Track on Datasets and Benchmarks. Source code at
https://github.com/corl-team/COR
Q-Ensemble for Offline RL: Don't Scale the Ensemble, Scale the Batch Size
Training large neural networks is known to be time-consuming, with the
learning duration taking days or even weeks. To address this problem,
large-batch optimization was introduced. This approach demonstrated that
scaling mini-batch sizes with appropriate learning rate adjustments can speed
up the training process by orders of magnitude. While long training time was
not typically a major issue for model-free deep offline RL algorithms, recently
introduced Q-ensemble methods achieving state-of-the-art performance made this
issue more relevant, notably extending the training duration. In this work, we
demonstrate how this class of methods can benefit from large-batch
optimization, which is commonly overlooked by the deep offline RL community. We
show that scaling the mini-batch size and naively adjusting the learning rate
allows for (1) a reduced size of the Q-ensemble, (2) stronger penalization of
out-of-distribution actions, and (3) improved convergence time, effectively
shortening training duration by 3-4x times on average.Comment: Accepted at 3rd Offline Reinforcement Learning Workshop at Neural
Information Processing Systems, 202
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