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 agents ought to take actions in an environment to maximize some notion of cumulative reward.
Key Concepts
- Agent: The decision-making entity in the environment.
- Environment: The context in which the agent operates.
- State: The current situation or context of the environment.
- Action: The decision made by the agent.
- Reward: The outcome of the action taken by the agent.
Types of Reinforcement Learning
- Tabular RL: The agent learns a table of Q-values for each state-action pair.
- Model-Based RL: The agent learns a model of the environment and uses it to plan its actions.
- Model-Free RL: The agent learns directly from experience without a model of the environment.
Common Algorithms
- Q-Learning: An algorithm that learns Q-values for each state-action pair.
- Deep Q-Network (DQN): A deep learning algorithm that combines Q-learning with a neural network.
- Policy Gradient Methods: Algorithms that learn a policy directly from the gradient of the expected reward.
Challenges
- Credit Assignment: Determining which actions contributed to the final reward.
- Exploration vs. Exploitation: Balancing the need to explore new actions with the need to exploit known good actions.
- Sample Efficiency: The number of samples needed to learn an effective policy.
Resources
For more information on Reinforcement Learning, check out our Reinforcement Learning Tutorial.
Reinforcement Learning Diagram