Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward. It's widely used in various applications like robotics, game playing, and autonomous systems.
Key Concepts in RL
1. Agent and Environment
- The agent is the learner or decision-maker.
- The environment is everything the agent interacts with.
2. Reward Signal
- The reward is a feedback signal that guides the agent's learning.
- It's a scalar value indicating the immediate benefit of an action.
3. Policy
- A policy defines the strategy that the agent employs to determine actions based on the current state.
- It can be deterministic or stochastic.
4. Value Function
- The value function estimates how good it is for an agent to be in a particular state or to take a specific action.
- It helps in making optimal decisions.
5. Model
- A model of the environment is a representation of how the environment behaves.
- It's optional in many RL algorithms.
Types of Reinforcement Learning
- Positive Reinforcement: Giving a reward to encourage a behavior.
- Negative Reinforcement: Removing an unpleasant stimulus to encourage a behavior.
- Punishment: Applying a penalty to discourage a behavior.
- Extinction: Withdrawing reinforcement to reduce a behavior.
Common Algorithms
- Q-Learning
- Deep Q-Networks (DQN)
- Policy Gradient Methods
- Actor-Critic Methods
Resources
For a deeper understanding of reinforcement learning and its applications, you can visit our Reinforcement Learning Overview page.