In this tutorial, we build a safety-critical reinforcement learning pipeline that learns entirely from fixed, offline data rather than live exploration. We design a custom environment, generate a ...

This important study uses reinforcement learning to study how turbulent odor stimuli should be processed to yield successful navigation. The authors find that there is an optimal memory length over ...

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 ...

Abstract: Planning a path is crucial for safe and efficient Unmanned aerial vehicle flights, especially in complex environments. While the Q-learning algorithm in reinforcement learning performs ...

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 ...

ABSTRACT: Double Q-learning has been shown to be effective in reinforcement learning scenarios when the reward system is stochastic. We apply the idea of double learning that this algorithm uses to ...

"This tutorial shows how to use PyTorch to train a DQN agent on the CartPole-v0 task from the [OpenAI Gym](https://gym.openai.com/).\n", "The agent has to decide ...