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. This guide provides an overview of the fundamental concepts and algorithms in RL.
Key Concepts
- Agent: The decision-making entity that interacts with the environment.
- Environment: The system with which the agent interacts. It provides the agent with information about the state of the environment.
- State: The current situation of the environment.
- Action: The decision made by the agent.
- Reward: The feedback signal from the environment to the agent.
- Policy: A mapping from states to actions that defines the behavior of the agent.
Common RL Algorithms
- Q-Learning: An online learning algorithm that approximates the Q-value function, which represents the expected utility of taking an action in a given state.
- Deep Q-Network (DQN): A combination of Q-learning and deep learning that allows the agent to learn policies from high-dimensional input spaces.
- Policy Gradient Methods: Algorithms that directly learn the policy from the environment without explicitly learning the value function.
- SARSA: A type of Q-learning that considers the next state and action when updating the Q-values.
Learning Resources
For further reading on RL, you may want to check out the following resources:
Images
Here are some images related to RL:
If you have any questions or need further clarification, please feel free to reach out to us.