Contents
What does reinforcement learning solve?
Reinforcement learning is an area of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation.
What is reinforcement learning and how it can be implemented in real world problems?
Reinforcement Learning is a machine learning framework that enables an agent to evaluate the current environment, take optimal action, and get feedback from the environment after each step to maximize returns.
When to use reinforcement learning?
Reinforcement learning is useful when you have no training data or specific enough expertise about the problem. On a high level, you know WHAT you want, but not really HOW to get there. After all, not even Lee Sedol knows how to beat himself in Go.
What are the types of reinforcement learning?
There are two types of reinforcement, known as positive reinforcement and negative reinforcement; positive is where by a reward is offered on expression of the wanted behaviour and negative is taking away an undesirable element in the persons environment whenever the desired behaviour is achieved.
How does reinforcement learning work?
Reinforcement Learning (RL) is a type of machine learning technique that enables an agent to learn in an interactive environment by trial and error using feedback from its own actions and experiences. Though both supervised and reinforcement learning use mapping between input and output,…
What is reinforcement learning model?
Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation.