In machine learning, machines are taught to mimic the thoughts, speech, and actions of humans. Just like humans, these machines can also interact with their environments. When an AI agent (the learner or decision-maker) interacts with its environment, the environment responds by giving it a reward or a penalty based on its performance. This feedback allows the agent to learn over time, similar to how humans learn from experience.
This learning paradigm is called Reinforcement Learning, and it is most commonly used in areas such as robotics, autonomous vehicles, and gaming.
To understand how RL works, we need to understand it's components:
Agent: The agent is the 'student' or the 'learner' that makes decisions and interacts with the environment.
Environment: This is the world where the agent lives. It includes everything that the agent interacts with. It can be the rea world or a virtual environment.
State: The state is the current position of the agent in the environment.
Action: After interacting with the environment, the agent takes an action.
Reward: The agent receives some reward from it's environment after performing an action. This reward is either positive or negative.
Policy: The policy is the agent's 'brain' which decides the relevant action to take.
For example in the game of chess, the player acts as the Agent while the board and rules represent the Environment. The State is the current arrangement of all pieces, and an Action is any move the agent chooses to make. The Reward is the feedback received at the end of the game, like a win or a loss. The Policy is the overall strategy the agent follows to decide which move is best to win.
RL can be further understood by looking at it's types:
Value-Based: In value-based methods, the agent will ask "How much reward will I get if I take Action X in State Y?" If the agent knows the value of all actions, it will simply choose the the highest-possible value.
Policy-Based: In policy-based methods instead of calculating a value, the agent learns the strategy. If an action led to a big win in the past, the agent will increase the probability of taking that action again in the future.
Model-Based: In a model-based method the agent tries to plan ahead. The agent tries to create a "map" or a mental model of how the environment works and acts accordingly.
In conclusion, Reinforcement Learning is the art of learning by doing. By using a cycle of trial and error, an AI agent learns how to navigate complex worlds and solve difficult problems through the simple language of rewards.