What is model reinforcement learning in PyTorch?

PyTorch’s model reinforcement learning is a reinforcement learning technology based on the deep learning framework PyTorch. In reinforcement learning, an agent learns how to make decisions to maximize cumulative rewards through interaction with the environment. PyTorch provides powerful deep learning tools and libraries that can be used to build and train reinforcement learning models, including deep Q networks (DQN), policy gradient methods, and more. With PyTorch’s model reinforcement learning, various complex reinforcement learning tasks can be achieved, such as gameplay, robot control, and more.