Reinforcement Learning (RL) is a branch of machine learning that focuses on how agents should take actions in an environment to maximize some notion of cumulative reward. The key elements of RL include an agent, an environment, actions, states, and rewards.
Key Components
- Agent: The decision-making entity that interacts with the environment.
- Environment: The system with which the agent interacts.
- Actions: The choices made by the agent.
- States: The situation in the environment at any given time.
- Rewards: The value associated with each state-action pair.
Types of RL
- Tabular RL: When the state and action spaces are discrete and finite, and the transition probabilities and rewards are known.
- Model-Based RL: When the agent maintains a model of the environment and uses it to make decisions.
- Model-Free RL: When the agent learns directly from the environment without a model.
Common Algorithms
- Q-Learning: A value-based algorithm that learns a Q-function, which represents the expected future reward of taking an action in a given state.
- Policy Gradient Methods: Learn a policy that directly specifies the probability of taking an action in a given state.
- SARSA: A reinforcement learning algorithm that learns the value function and policy simultaneously.
Application Areas
Reinforcement Learning has been successfully applied in various fields, such as robotics, game playing, autonomous driving, and finance.
Example
Let's say you are playing a video game. In this case, the agent is you, the environment is the game world, the actions are your controls, the states are the positions and statuses of your character and the enemies, and the rewards are the points you earn.
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Reinforcement Learning in Video Games
To learn more about reinforcement learning, you can visit our Reinforcement Learning Tutorial.