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2. Q learning vs Deep Q learning
Qlearning and Deep Qlearning are both methods used in reinforcement learning, but they differ significantly in how they handle the Qvalue function and the types of problems they can address. Here’s a detailed comparison:
QLearning
 Definition: Qlearning is a modelfree reinforcement learning algorithm that aims to learn the quality (Qvalue) of actions, telling an agent what action to take under what circumstances.
 QValue Function: The Qvalue function Q(s,a)Q(s, a)Q(s,a) is typically represented as a table (Qtable) where sss is a state and aaa is an action.
 Algorithm: It updates Qvalues based on the Bellman equation:
Q(s, a) ← Q(s, a) + α[r + γ * max_{a'} Q(s', a') − Q(s, a)]
Here, α is the learning rate, r is the reward, γ is the discount factor, and s' is the next state.  Suitability: Suitable for problems with a relatively small stateaction space where maintaining a Qtable is feasible.
 Limitations: Struggles with large or continuous state spaces due to the curse of dimensionality; requires a lot of memory and computational power as the stateaction space grows.
Deep QLearning (DQN)
 Definition: Deep Qlearning is an extension of Qlearning that uses a deep neural network to approximate the Qvalue function, allowing it to handle large and complex state spaces.
 QValue Function: The Qvalue function Q(s,a)Q(s, a)Q(s,a) is approximated using a neural network, where the input is the state sss and the outputs are Qvalues for each possible action aaa.
 Algorithm: It uses experience replay and target networks to stabilize training:
 Experience Replay: Stores the agent's experiences (state, action, reward, next state) in a replay buffer and samples minibatches of experiences to train the neural network, breaking the correlation between consecutive experiences.
 Target Network: Maintains a separate target network with the same architecture as the Qnetwork, which is updated less frequently to provide stable targets for training.
 Algorithm Update:
Q(s, a) ← Q(s, a) + α [r + γ * max_{a'} Q_{target}(s', a') − Q(s, a)]
Here, Q_{target} is the Qvalue from the target network.  Suitability: Suitable for problems with large or continuous state spaces, such as video games or robotic control tasks.
 Advantages: Can handle highdimensional input spaces (e.g., images); generalizes better to unseen states.
 Challenges: Requires more computational resources for training the neural network; can be harder to tune and stabilize.