marktechpost

A Coding Implementation to Train Safety-Critical Reinforcement Learning Agents Offline Using Conservative Q-Learning with d3rlpy and Fixed Historical Data

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 ...
IEEE

Decoupled Online Q-Learning Design for MIMO Active Magnetic Compensation Systems

Abstract: The inherent cross-coupling phenomenon in three-input three-output active magnetic compensation (AMC) systems employing multiaxial coil configurations hinders precise system characterization ...
IEEE

Improved Q-Learning Algorithm Based on Flower Pollination Algorithm and Tabulation Method for Unmanned Aerial Vehicle Path Planning

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 ...
GitHub

Create easier tutorial on using (Async)VectorEnvs

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 ...
GitHub

Reinforcement (Q-)Learning with PyTorch.ipynb

"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 ...