Machine Learning  Reinforcement Learning Algorithms Reinforcement Learning Concepts Reinforcement learning is a type of machine learning where an agent learns to interact with an environment by taking actions and receiving rewards or punishments. Learning a policy that maximizes the cumulative reward across a series of actions is the aim of reinforcement learning. Two common reinforcement learning algorithms are Q-learning and Deep Q-Networks (DQNs). Q-learning r einforcement learning algorithm Q-learning is a model-free, off-policy reinforcement learning algorithm. In Q-learning, the agent learns an action-value function, called a Q-function, which estimates the expected cumulative reward for taking a particular action in a particular state. The Q-function can be represented as a lookup table or a neural network. The Q-function is updated using the Bellman equation: Q(s,a) = Q(s,a) + α(r + γmax(Q(s',a')) - Q(s,a)) where Q(s, a) is the Q-value for taking action an in stat