Welcome to the Reinforcement Learning section of our documentation. Here, you will find a comprehensive guide to understanding and implementing reinforcement learning algorithms.
What is Reinforcement Learning?
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. The agent learns from the consequences of its actions, which are represented by rewards or penalties.
Key Concepts
- Agent: The decision-making entity in the environment.
- Environment: The context in which the agent operates.
- State: The current situation of the environment.
- Action: The decision made by the agent.
- Reward: The feedback received by the agent after taking an action.
Types of Reinforcement Learning Algorithms
Value-based RL: Focuses on learning the value of states or state-action pairs.
- Q-Learning
- Deep Q-Network (DQN)
Policy-based RL: Focuses on learning a policy that maps states to actions.
- Policy Gradient Methods
- Actor-Critic Methods
Getting Started
To get started with reinforcement learning, you might want to check out our Getting Started Guide.
Community Resources
Join our Reinforcement Learning Community to connect with other learners and experts.
Conclusion
Reinforcement Learning is a powerful tool for creating intelligent agents that can learn from their environment. By understanding the key concepts and algorithms, you can start building your own RL applications.
For more information, don't forget to explore our Documentation.