Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve a goal. It is a subset of machine learning that focuses on how software agents ought to take actions in an environment to maximize some notion of cumulative reward.
Key Components of Reinforcement Learning
- Agent: The decision-making entity that learns from the environment.
- Environment: The surroundings in which the agent operates.
- State: The current situation or condition of the environment.
- Action: The decision made by the agent.
- Reward: The feedback given to the agent for its action.
Types of Reinforcement Learning
- Tabular RL: The agent learns from a table of states and actions.
- Model-based RL: The agent builds a model of the environment and uses it to make decisions.
- Model-free RL: The agent learns directly from the environment without building a model.
Example: Q-Learning
Q-Learning is a popular algorithm in reinforcement learning. It is a model-free algorithm that learns the value of each state-action pair.
- Q-Table: A table that stores the expected value of each state-action pair.
- Learning Rate: The rate at which the agent updates its Q-values.
- Discount Factor: The factor that determines how much future rewards are worth.
Resources
For more information on reinforcement learning, you can visit our Reinforcement Learning Tutorial.
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