Reinforcement learning is a branch of machine learning where an agent learns to make sequences of decisions by interacting with an environment and receiving rewards or penalties. Instead of being told the correct action, the agent discovers effective behavior through trial and error. At its core is the concept of a policy, which maps states to actions, and a value function, which estimates expected future rewards.

The research emphasizes Markov decision processes as the formal framework for reinforcement learning. In this setting, each decision depends only on the current state, not on the full history. The agent seeks to maximize the long term cumulative reward, often discounted so that immediate rewards matter more than distant ones.

Two main families of methods are discussed. Value based methods, such as Q learning, try to learn the value of state action pairs and choose actions that appear best. Policy based methods optimize the policy parameters directly, often using gradient techniques. Actor critic algorithms combine both ideas by maintaining an explicit policy and a value function that evaluates it.

Function approximation, especially with deep neural networks, is crucial for handling complex or continuous state spaces. This allows reinforcement learning to scale to problems such as game playing, robotics and control. Exploration strategies, like epsilon greedy choices or more principled approaches based on uncertainty, are central to balancing the tradeoff between trying new actions and exploiting known good ones. The research also stresses practical challenges, including stability, sample efficiency and safety, and presents reinforcement learning as a flexible framework for sequential decision making under uncertainty.