The primary goal of an agent in Reinforcement Learning is to maximize the cumulative reward it receives over time by taking actions in an environment.

Q-Learning is an off-policy Reinforcement Learning algorithm that estimates the optimal action-value function for a given task. It is known for its simplicity and effectiveness in various domains.

The learning rate parameter in Q-Learning controls the step size for updating the Q-values. A higher learning rate leads to faster convergence but may result in instability, while a lower learning rate ensures stability but slower convergence.

The key difference between Q-Learning and SARSA is that Q-Learning is an off-policy algorithm, meaning it can learn from experiences generated by any policy, while SARSA is an on-policy algorithm, meaning it can only learn from experiences generated by the current policy.

Deep Q-Network (DQN) is a Reinforcement Learning algorithm that combines the power of deep neural networks with Q-Learning. It uses a deep neural network to approximate the Q-function and can handle large and complex state spaces.

In Deep Q-Network, the target network provides a stable estimate of the Q-values for calculating the target values during training. This helps in stabilizing the learning process and reducing overfitting.

The primary challenge in Reinforcement Learning is balancing between exploring new actions to find potentially better policies and exploiting known good actions to maximize immediate rewards. This is known as the exploration vs exploitation dilemma.

Epsilon-greedy is an exploration strategy in Reinforcement Learning that aims to balance exploration and exploitation by gradually reducing the probability of taking random actions as the agent gains more experience.

In Reinforcement Learning, the discount factor is used to weight the importance of future rewards relative to immediate rewards. It allows the agent to consider the long-term consequences of its actions and make decisions accordingly.

Policy Gradient is a Reinforcement Learning algorithm that is well-suited for handling continuous action spaces. It directly optimizes the policy function to maximize the expected cumulative reward.

In Reinforcement Learning, a critic network is used to evaluate the value of the current state or state-action pair. This information is then used by the actor network to select the next action to be taken.

Actor-Critic is a Reinforcement Learning algorithm that is commonly used in robotics and control problems. It combines an actor network, which generates actions, with a critic network, which evaluates the value of states or state-action pairs.

Policy Iteration is a Reinforcement Learning algorithm that gradually improves the policy by interacting with the environment and learning from the consequences of actions. It alternates between policy evaluation and policy improvement steps.

Hierarchical Reinforcement Learning is a Reinforcement Learning algorithm that is designed to learn hierarchical policies. It decomposes a complex task into a hierarchy of subtasks and learns policies for each subtask.

Generalization in Reinforcement Learning refers to the process of using past experiences to make predictions about future outcomes. It allows the agent to learn from a limited number of experiences and apply that knowledge to new situations.