README Tutos

Check the tutorials on torchrl documentation: https://pytorch.org/rl

Get started with logging

Get started with logging

Get started with Environments, TED and transforms

Get started with Environments, TED and transforms

Get started with data collection and storage

Get started with data collection and storage

Getting started with model optimization

Getting started with model optimization

Using pretrained models

Using pretrained models

Get started with your own first training loop

Get started with your own first training loop

Get started with TorchRL’s modules

Get started with TorchRL's modules

Task-specific policy in multi-task environments

Task-specific policy in multi-task environments

Recurrent DQN: Training recurrent policies

Recurrent DQN: Training recurrent policies

Multi-Agent Reinforcement Learning (PPO) with TorchRL Tutorial

Multi-Agent Reinforcement Learning (PPO) with TorchRL Tutorial

TorchRL envs

TorchRL envs

Reinforcement Learning (PPO) with TorchRL Tutorial

Reinforcement Learning (PPO) with TorchRL Tutorial

Using Replay Buffers

Using Replay Buffers

TorchRL trainer: A DQN example

TorchRL trainer: A DQN example

Pendulum: Writing your environment and transforms with TorchRL

Pendulum: Writing your environment and transforms with TorchRL

Introduction to TorchRL

Introduction to TorchRL

Competitive Multi-Agent Reinforcement Learning (DDPG) with TorchRL Tutorial

Competitive Multi-Agent Reinforcement Learning (DDPG) with TorchRL Tutorial

TorchRL objectives: Coding a DDPG loss

TorchRL objectives: Coding a DDPG loss