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2. Q learning vs Deep Q learning

Q-learning and Deep Q-learning are both methods used in reinforcement learning, but they differ significantly in how they handle the Q-value function and the types of problems they can address. Here’s a detailed comparison:

Q-Learning

Definition: Q-learning is a model-free reinforcement learning algorithm that aims to learn the quality (Q-value) of actions, telling an agent what action to take under what circumstances.
Q-Value Function: The Q-value function Q(s,a)Q(s, a)Q(s,a) is typically represented as a table (Q-table) where sss is a state and aaa is an action.
Algorithm: It updates Q-values 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 state-action space where maintaining a Q-table 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 state-action space grows.

Deep Q-Learning (DQN)

Definition: Deep Q-learning is an extension of Q-learning that uses a deep neural network to approximate the Q-value function, allowing it to handle large and complex state spaces.
Q-Value Function: The Q-value 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 Q-values 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 mini-batches 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 Q-network, 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 Q-value 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 high-dimensional 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.

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