Deep Reinforcement Learning (DRL) is a rapidly evolving field, and PyTorch is one of the most popular frameworks for implementing DRL algorithms. Below are some examples of DRL with PyTorch.

  • Q-Learning: A simple example of implementing Q-Learning for a cart-pole environment.
  • Deep Q-Network (DQN): An example of using DQN to train an agent to play Atari games.
  • Policy Gradient Methods: An example of using Policy Gradient methods for a simple maze navigation problem.

For more detailed examples and tutorials, check out our DRL PyTorch Tutorials.

Images

Here are some images related to DRL and PyTorch:

Deep_Reward_Learning
Policy_Gradient
DQN_Atari