Another type of machine learning is the reinforcement learning.
Reinforcement learning.
RL is a branch of machine learning that focuses on how agents can learn to make decisions through trial
and error to maximize cumulative rewards.
RL allows machines to learn by interacting with an environment and receiving feedback based on their
actions.
This feedback comes in the form of rewards or penalties.
Reinforcement learning revolves around the idea that an agent, the learner or decision maker, interacts
with an environment to achieve a goal.
So this is the environment or the agent.
This is the raw data.
The raw data is given to the agent and the environment.
Based on this agent, the prediction is made.
The agent performs actions and receives feedback to optimize its decision making.
Over time.
The agent the decision maker that performs the actions.
Environment the word or system on which the agent operates.
State is the situation or condition the agent is currently in action, the possible moves or decisions
the agent can make, and the reward the feedback or result from the environment based on the agent's
action on.
Under unsupervised learning or reinforcement learning, machines learn by trial and error using reinforcement
learning.
So here guys, adapting its approach to the situation based on previous experiences helps it achieve
the best outcome.
This is the main summary of the reinforcement learning.
As a quick recap for how reinforcement learning works, the agent interacts Iteratively with its environment.
In the feedback, the agent observes the current state of the environment it chooses and performs an
action based on its policy.
The environment responds by transitioning to a new state and providing a reward or penalty.
The agent updates its knowledge, policy, or values or value function based on the reward received
and the new state.
This cycle repeats with the agent balancing exploration, trying new actions, and exploiting using
known good actions to maximize the cumulative reward over time.
This process is mathematically framed as a Markov decision process MDP, where future states depend
only on the current state and action, not on the prior sequence or events.