vahvistusmenetelmillä
Vahvistusmenetelmät, also known as reinforcement methods, is a concept in machine learning and artificial intelligence. It refers to a class of algorithms that learn how to make a sequence of decisions by trying to maximize a reward signal. The core idea is that an agent interacts with an environment, takes actions, and receives feedback in the form of rewards or penalties. Through repeated trial and error, the agent learns which actions lead to desirable outcomes and which do not, gradually improving its strategy over time.
The agent operates in an environment that can be in various states. When the agent performs an
Key components of reinforcement learning include the agent, the environment, states, actions, and rewards. Algorithms like