Reinforcement learning (RL) is a type of machine learning paradigm where an agent learns to make decisions by interacting with its environment. The agent makes an action and receives feedback in the form of rewards, which it uses to improve its decision-making over time.

Basic concepts:

1. Agent: The action taker.
2. Environment: The external system with which the agent interacts. The environment provides feedback to the agent based on the actions it takes.
3. State ( s ): A representation of the current situation of the agent in the environment.
4. Action ( $A _s$ ): The decision or move that the agent makes at a particular state.
5. Reward ( $R _s$ ): A numerical value that the agent receives as feedback from the environment after taking an action. The goal of the agent is typically to maximize the cumulative reward over time.
6. Value ( $V _s$ ): The expected cumulative future reward.
7. Policy ( $\pi$ ): The strategy or set of rules that the agent uses to determine its actions in a given state.

The agent’s goal is to discover an optimal policy that leads to the maximum cumulative reward over the long term. It does so by trial and error learning, adjusting its policy based on the rewards it receives. The agent can decide on an action in a given state by using either exploitation, exploration, or a combination of both strategies. By using exploitation, an agent makes a decision based on its past experiences and prefers actions it has tried and found to be effective in producing reward. By using exploration, an agent makes a decision while disregarding past experiences, in order to expose itself to different actions. The trade-off between exploration and exploitation is an important concept of reinforcement learning - the agent has to exploit what it has already experienced in order to obtain reward, but it also has to explore in order to make better action selections in the future.