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. This tutorial will give you a basic understanding of RL concepts and techniques.
Key Concepts
- Agent: The decision-making entity in the environment.
- Environment: The system with which the agent interacts.
- State: The situation or condition of the environment at any given time.
- Action: The decision made by the agent to change the state of the environment.
- Reward: The feedback received by the agent after performing an action.
Types of RL Algorithms
- Value-based RL: The agent learns the value of each state-action pair.
- Policy-based RL: The agent learns a policy that maps states to actions.
- Model-based RL: The agent learns a model of the environment and uses it to plan actions.
Example
Here's a simple example of a Reinforcement Learning problem:
Problem: A robot is placed in a grid with a goal of reaching the top-right corner. The robot can move up, down, left, or right. If the robot steps on a cell with a negative reward, it receives a penalty.
Solution: The robot uses a Reinforcement Learning algorithm to learn the optimal path to the goal.
Further Reading
For more information on Reinforcement Learning, check out our Advanced Reinforcement Learning Tutorial.
Images
Reinforcement Learning Robot