Answer / Swati Chauhan

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with its environment, receiving rewards or punishments for certain actions. Here are some popular RL algorithms:
1. Q-Learning: A model-free algorithm that uses the Bellman equation to approximate the value function.
2. SARSA (State-Action-Reward-State-Action): An on-policy algorithm that updates the Q-value by sampling the state, action, reward, and next state sequentially.
3. Deep Q-Network (DQN): A deep learning-based extension of Q-Learning that uses a neural network to approximate the Q-function for high-dimensional state spaces.
4. Policy Gradients: A policy optimization method that optimizes the policy directly instead of approximating the value function.

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