Reinforcement learning is a type of machine learning where an agent learns by interacting with an environment
and receiving rewards or penaltiesfor its actions
Key components of reinforcement learning are the agent, the environment, and the reward system.
The agent learns by trial and error, making decisions and adjusting its behavior based on the outcomes.
Reinforcement learning problems are often formulated as MDPs, which define the state space, action space, transition probabilities, and reward function.
The agent's strategy for selecting actions is called its policy.
The value function measures the expected future reward from a given state.
Common reinforcement learning algorithms include Q-learning, SARSA, and Deep Q-Networks (DQN).
Reinforcement learning has been applied to various domains, such as game playing, robotics, and autonomous systems.