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.

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 ...

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 ...

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 ...

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.

A new research paper titled “Discovering faster matrix multiplication algorithms with reinforcement learning” was published by researchers at DeepMind. “Here we report a deep reinforcement learning ...

Motor Synergy Development in High-Performing Deep Reinforcement Learning Algorithms. IEEE Robotics and Automation Letters , 2020; 5 (2): 1271 DOI: 10.1109/LRA.2020.2968067 Cite This Page : ...

In their paper, DeepMind’s scientists make the claim that the reward hypothesis can be implemented with reinforcement learning algorithms, a branch of AI in which an agent gradually develops its ...