Reinforcement Learning

Reinforcement learning is a type of machine learning where the model is trained on a reward system. The model is trained to learn the relationship between the input data and the output data with a reward system.

Reward System

The reward system is a system that rewards the model for making the correct decision. The reward system is a system that rewards the model for making the correct decision. The reward system is a system that rewards the model for making the correct decision.

Sample architecture

%%{init: {"flowchart": {"htmlLabels": false}} }%% graph TD A["Environment"] --> B["Agent"] B --> C["Action"] C --> D["Environment"] D --> E["State"] E --> B D --> F["Reward"] F --> B style A fill:#f9f,stroke:#333,stroke-width:2px style B fill:#bbf,stroke:#333,stroke-width:2px style C fill:#dfd,stroke:#333,stroke-width:2px style D fill:#f9f,stroke:#333,stroke-width:2px style E fill:#fdd,stroke:#333,stroke-width:2px style F fill:#ff9,stroke:#333,stroke-width:2px

This architecture is a simple example of a reinforcement learning architecture. The agent is the model that is trained to learn the relationship between the input data and the output data. The environment is the system that the agent is interacting with. The action is the decision that the agent makes. The state is the current state of the environment. The reward is the reward that the agent receives for making the correct decision.

This can be used in the training process transforming the reward into a loss function that can be used to train the model.