Reenforcement leraring : is an area of machine learing conceted with how software agents ought to take actions in an evniroment in order to maximumze the notion of cumulative reward.

or RL is teaching a software agent how to behave in an environment by telling it how good its doing.

loss_fn = > bellman

model used

this game uses Deep Q-Network(DQN) model for Reinforcement learing (RL) .

overview:what is DQN?

• deep q networks are a type of reinformcement learning model where an agent learns to take actions in an environment to maximize cumulative rewards overt time.

• instead fo maintaing a Q table ( used in traditinal Q-learning) , the agent uses a neural network to approximate the Q-values for state action pairs.

components of the code

linear_qnet class this class defines the nerual network architecture for the Q function aproximation.

** attributes**

• linear1: a fully connected (dense) layer that mps the input_size to a hidden layer of size hidden_size

• linear2: another fully connected layer that maps the hidden layer to the output_size, represnting q-values for all possible actions.

forward pass

• input X goes through linear1 and applies the RELU activation function
• the output of linear1 is passed to linear2 to compute the final q-values. save method
• saves the models weights (state_dict()) toa file (model.pth) i a directory called ./model ** QTrainer Class **

this class hanles the training process for th model using Q-learning principles ** attributes:**

• model: the neural network ( an instance of Linear_qnet).
• lr: learning rate for the optimizer.
• gamma: discount factor for future rewards.
• optimzer: an adam optimzer that updates the model parameters during training .
• criterion: the loss fuction , specifically mean squred error to calcuate the difference etween predicted q-values and target q-values.