DQN Coding | DQN code implementation | Deep Q-Network

this section explains the Deep Q Network dqn model and code implementation let's first look at the Deep Q Network model overview dqn used to train an agent to maximize rewards by learning an optimal policy it consists of a neural network-based function approximator an experience replay buffer and a Target Network to stabilize training Q learning q-learning is a model-free value-based RL algorithm that estimates the actual value function qsa as follows it updates Q values using the Bellman equation as follows deep Q Network traditional Q learning fails in high dimensional environment since one large State action space requires huge memory two generalization issues cannot handle unseen States solution use a neural Network to approximate qsa leading to deep Q networks deep Q Network formula derivation instead of maintaining a q table dqn learns the function qsa using deep learning the loss function is as follows Q policy is current Q Network predicts current Q values Q Target is Target Q Network provides stable learning Target Theta is policy Network weights Theta minus is Target Network weights periodically copied from policy network key features of dqn one experience replay stores past experiences in a buffer samples random mini batches during training breaks correlation in data two target Network uses a separate Target Q network with frozen parameters reduces training instability by updating every end steps three Epsilon greedy policy balances exploration random action versus exploitation choosing best action now we implement the Deep Q Network code dqn class replay buffer class dqn agent class e e e e testing the Deep Q Network model e e e