The RL Process

- Overview

Reinforcement learning (RL) is a machine learning (ML) process where an "agent" learns to make decisions by interacting with an environment, receiving feedback in the form of rewards for positive actions and penalties for negative ones, gradually optimizing its behavior to maximize long-term rewards through trial and error; essentially, it mimics how animals learn through experience, adapting their actions based on the consequences they encounter. 

Key components of the reinforcement learning process:

• Agent: The decision-making entity that interacts with the environment, taking actions and observing the results.
• Environment: The space where the agent operates, providing feedback (states) based on the agent's actions.
• State: The current situation or condition of the environment that the agent perceives.
• Action: A choice that the agent can make within a given state.
• Reward: Positive feedback given to the agent for taking a desirable action.
• Policy: The strategy that the agent uses to decide which action to take in each state, which is learned over time through experience.

- How RL Works

• Initialize: The agent starts in a given state within the environment.
• Take action: The agent selects an action based on its current policy.
• Observe result: The environment transitions to a new state and provides a reward signal based on the agent's action.
• Update policy: The agent uses the feedback (reward) to update its policy, aiming to choose actions that lead to higher rewards in the future.
• Repeat: This process of taking actions, observing results, and updating the policy continues until the agent has learned an optimal policy to maximize long-term rewards.

RL models are taught to make a series of judgments by learning. In unpredictable and potentially complex environments, agents must learn to achieve goals. AI is placed in a game-like environment while learning reinforcement. To find solutions to problems, computers use trial and error.

AI is rewarded or punished for the actions it takes in order for it to do what its programmers want it to do. The aim is to maximize the total winnings as much as possible. 

Although the designer made the reward policy (i.e. the rules of the game), he/she gave the model no hints or ideas on how to solve the game.

Starting with completely random trials, and progressing to complex strategies and superhuman skills, it is up to the model to figure out how to complete the task to maximize reward. Reinforcement learning is currently the most effective technique for implying machine creativity by harnessing the power of search and multiple trials. 

Unlike humans, AI may gain experience from thousands of simultaneous games if a RL algorithm is executed on a powerful computer infrastructure.

- Example

Let's imagine an agent learning to play Super Mario Bros. (Super Mario Bros. is a platform game developed and published by Nintendo for the Nintendo Entertainment System) as a working example. 

The RL process can be modeled as a loop, which works as follows:

• Our agent receives state S0 from the environment (in our case we receive the first frame of the game (state) from Super Mario Bros. (environment))
• Based on this state S0, the agent takes action A0 (our agent will move to the right)
• The environment transitions to a new state S1 (new frame)
• The environment gives the agent some reward R1 (not dead: +1)