Dqn-implementation-pytorch

The network approximates the Q-value function, mapping states to the expected rewards of each possible action.

Deep Q-Networks (DQN) combine Q-Learning with Deep Neural Networks to solve environments with high-dimensional state spaces. Implementing a robust DQN in PyTorch involves managing several moving parts: the neural network architecture, experience replay, target networks, and the training loop. 1. Define the Q-Network Architecture dqn-implementation-pytorch

The agent manages two identical networks: the (active learning) and the Target Network (stable targets). The network approximates the Q-value function