Reinforcement learning focuses on rewarding desired AI actions and punishing undesired ones. Common RL algorithms include State-action-reward-state-action, Q-learning, and Deep-Q networks. RL ...

For example, Q-learning, a classic type of reinforcement learning algorithm, creates a table of state-action-reward values as the agent interacts with the environment.

For example, Q-learning, a classic type of reinforcement learning algorithm, creates a table of state-action-reward values as the agent interacts with the environment.

Indeed, the first application in which reinforcement learning gained notoriety was when AlphaGo, a machine learning algorithm, won against one of the world’s best human players in the game Go.

So, reinforcement learning algorithms have all the same philosophical limitations as regular machine learning algorithms. These are already well-known by machine learning scientists.

An example of this is DeepMind’s MuZero algorithm, a deep reinforcement learning algorithm that’s able to construct agents that can plan out how to play games such as chess and GO, ...

If you want to get into the weeds with reinforcement learning algorithms and theory, and you are comfortable with Markov decision processes, I’d recommend Reinforcement Learning: An Introduction ...

A reinforcement learning algorithm uses the reward function to tune a neural network based on the function’s scores. The initial trials will fail, as the pendulum keeps falling.

There are many different types of reinforcement learning algorithms, but two main categories are “model-based” and “model-free” RL. They are both inspired by our understanding of learning ...