Reinforcement Learning (DQN) Tutorial - PyTorch
This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent on the CartPole-v1 task from Gymnasium. You might find it helpful to read the original Deep Q Learning (DQN) …
Reinforcement Learning (PPO) with TorchRL Tutorial
Various libraries provide simulation environments for reinforcement learning, including Gymnasium (previously OpenAI Gym), DeepMind control suite, and many others.
TorchRL — torchrl 0.0 documentation
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. You can install TorchRL directly from PyPI (see more about installation instructions in the dedicated section …
Train a Mario-playing RL Agent — PyTorch Tutorials 2.9.0+cu128 ...
This tutorial walks you through the fundamentals of Deep Reinforcement Learning. At the end, you will implement an AI-powered Mario (using Double Deep Q-Networks) that can play the …
Multi-Agent Reinforcement Learning (PPO) with TorchRL Tutorial
This tutorial demonstrates how to use PyTorch and torchrl to solve a Multi-Agent Reinforcement Learning (MARL) problem. For ease of use, this tutorial will follow the general structure of the …
TorchRL — torchrl main documentation
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. You can install TorchRL directly from PyPI (see more about installation instructions in the dedicated section …
Competitive Multi-Agent Reinforcement Learning (DDPG) with
This tutorial demonstrates how to use PyTorch and TorchRL to solve a Competitive Multi-Agent Reinforcement Learning (MARL) problem. For ease of use, this tutorial will follow the general …
Reinforcement Learning (PPO) with TorchRL Tutorial
Various libraries provide simulation environments for reinforcement learning, including Gymnasium (previously OpenAI Gym), DeepMind control suite, and many others.
Welcome to PyTorch Tutorials — PyTorch Tutorials 2.9.0+cu128 …
Learn how to train semi-supervised learning algorithms (on custom data) using USB and PyTorch.
PyTorch – PyTorch
It supports a broad range of use cases—from natural language processing and computer vision to reinforcement learning and generative AI—through a robust ecosystem of libraries, tools, and …