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 of RL.
Key Concepts
- Agent: The decision-making entity that interacts with the environment.
- Environment: The system with which the agent interacts. It provides feedback in the form of rewards and observations.
- State: A representation of the environment's condition at a particular time.
- Action: A choice made by the agent.
- Reward: A signal from the environment indicating how well the agent's action was received.
Types of RL
- Tabular RL: The agent has a complete and discrete state space.
- Model-based RL: The agent has a model of the environment, which allows it to plan ahead.
- Model-free RL: The agent learns from the environment without a model.
Algorithms
- Q-Learning: A value-based method that learns the optimal action-value function.
- Policy Gradient: A policy-based method that learns the optimal policy directly.
- Deep Q-Network (DQN): A combination of Q-learning and deep learning, used for complex environments.
Resources
For more information on RL, you can visit the following resources:
Example
Here's an example of how to use a Q-table in Q-learning:
| State | Action 1 | Action 2 |
|-------|----------|----------|
| State 1 | 0.5 | 0.3 |
| State 2 | 0.4 | 0.6 |
For further reading, check out our Introduction to Q-Learning guide.