Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions in an environment to maximize a cumulative reward over time. The agent interacts with the environment by taking actions and receiving feedback in the form of reward signals. The goal is for the agent to learn a policy that maps each state to an action that maximizes the expected reward.
Markov Decision Processes (MDPs) are commonly used to model RL problems. An MDP is a mathematical framework that models decision-making problems as a set of states, actions, and rewards. The underlying assumption is that the next state and reward depend only on the current state and action, and not on any previous events. The agent’s goal is to find an optimal policy that maximizes the expected cumulative reward.
Q-learning is a popular algorithm for RL that is used to learn the optimal action-value function, which measures the expected reward of taking an action in a given state and following an optimal policy thereafter. The Q-value for a state-action pair is updated iteratively using the Bellman equation. Q-learning involves the exploration-exploitation tradeoff, where the agent tries to explore new states while also exploiting its current knowledge to maximize rewards.
Policy gradients is another popular approach to RL that directly optimizes a policy, rather than an action-value function. Policy gradients aim to maximize the expected cumulative reward by updating the policy gradients in the direction of gradients that maximize the expected reward. The update rule is computed using the score function estimator that estimates the gradient of the policy parameters with respect to the expected reward.
Overall, RL is an iterative process where the agent learns to make better decisions over time by interacting with the environment and learning from the feedback it receives. By modeling problems as MDPs and using algorithms like Q-learning and policy gradients, RL agents are able to learn complex decision-making tasks in a wide range of applications, including games, robotics, and industrial control systems.