What is reinforcement learning?
Q: What is reinforcement learning?
A: Reinforcement learning is a type of machine learning in which a software agent is taught to behave in an environment by receiving feedback about its performance.
Q: How is reinforcement learning different from supervised learning?
A: Reinforcement learning differs from supervised learning in that the correct inputs and outputs are never shown to the agent, and it usually learns as it goes rather than being trained in advance.
Q: What is the main inspiration for reinforcement learning?
A: Reinforcement learning is inspired by behaviorist psychology, which tries to explain behavior as a response to external stimuli.
Q: How does reinforcement learning work?
A: In reinforcement learning, the agent interacts with the environment and receives feedback in the form of rewards or punishments. It then uses this feedback to learn which actions will lead to better outcomes.
Q: What is online learning?
A: Online learning is a type of learning in which the agent learns as it goes, rather than being trained in advance.
Q: What is the challenge faced by agents in reinforcement learning?
A: The main challenge faced by agents in reinforcement learning is choosing between exploring the environment to learn more, or sticking with what they know works best.
Q: What is the goal of reinforcement learning?
A: The goal of reinforcement learning is to teach a software agent to behave in an environment in a way that maximizes its reward over time.
