Reinforcement learning algorithm
- First, the agent interacts with the environment by performing an action.
- The agent performs an action and moves from one state to another.
- Then the agent will receive a reward based on the action it performed.
- Based on the reward, the agent will understand whether the action is good or bad.
- If the action was good, that is, if the agent received a positive reward, then the agent will prefer performing that action, else the agent will try performing other actions that can result in a positive reward. So reinforcement learning is basically a trial-and-error learning process.