Reinforcement Learning

Reinforcement learning is a type of ML that involves an agent interacting with an environment and learning to take actions that maximize a reward signal. The process involves the agent taking actions in the environment, receiving feedback in the form of rewards, and learning from the feedback to adjust its policy or strategy. The agent aims to learn the optimal policy that maximizes the cumulative reward over time.

Reinforcement learning algorithms typically use a trial-and-error approach to learn the optimal policy. The agent starts with a random or default policy and interacts with the environment, receiving feedback in the form of rewards. Over time, the agent updates its policy based on the feedback to maximize the cumulative reward.

One of the most common algorithms used in reinforcement learning is Q-learning. Q-learning is a model-free, online learning algorithm that learns the optimal action-value function that maps each state-action pair to its expected cumulative reward. The agent uses the action-value function to select the best action to take in each state.

Reinforcement learning is used in a wide range of applications, including robotics, gaming, and autonomous systems. However, it can be challenging to implement and requires careful tuning of hyperparameters to achieve optimal performance.