Abstract: Deep Q-learning is an important reinforcement learning algorithm, which involves training a deep neural network, called deep Q-network, to approximate the well-known Q-function. Although ...

This project demonstrates a practical application of reinforcement learning in education. The system adapts to each student's knowledge level and learning style, recommending appropriate content in ...

Abstract: We propose a new Q-learning-based air-fuel ratio (AFR) controller for a Wankel rotary engine. We first present a mean-value engine model (MVEM) that is modified based on the rotary engine ...

Anyone interested in using Amazon Q, a generative AI assistant for developers and businesses, now has more free tools to help them get up to speed—regardless of whether they have technical experience.

Create a more basic tutorial on using (Async)VectorEnvs and why you should learn them. I would say that perhaps taking the already excellent blackjact_agent tutorial and rewriting is using AsyncEnvs ...

Calculating and predicting drug-target interactions (DTIs) is a crucial step in the field of novel drug discovery. Nowadays, many models have improved the prediction performance of DTIs by fusing ...

Aiming at the dimension disaster problem, poor model generalization ability and deadlock problem in special obstacles environment caused by the increase of state information in the local path planning ...