Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward. Unlike supervised learning, RL does not rely on labeled input/output pairs but instead learns from the consequences of actions.

• Agent: The learner or decision-maker.
• Environment: Everything the agent interacts with.
• Action: Choices made by the agent that affect the environment.
• State: A snapshot of the environment at a specific time.
• Reward: Feedback from the environment based on the action taken.

The reinforcement learning process can be summarized as follows:

                graph TD;
                    A[Start] --> B[Agent observes state]
                    B --> C[Agent selects action]
                    C --> D[Environment responds with reward]
                    D --> E[Agent updates knowledge]
                    E --> B;
                

• Define clear goals and rewards for the agent.
• Implement exploration strategies to balance exploration and exploitation.
• Use appropriate algorithms (e.g., Q-Learning, DDPG) based on the problem.
• Regularly evaluate the agent's performance and adjust parameters.