In order to choose the right reinforcement learning (RL) algorithm, it's important to understand the characteristics of your problem. Ask yourself questions such as what is the goal of the agent ...

All RL algorithms have common terms that need to be understood before diving into the algorithms. Notably, the RL approach to AI was taken further by OpenAI when they introduced reinforcement learning ...

Traditional multi-agent reinforcement learning algorithms are not scalable to environments with more than a few agents, since these algorithms are exponential in the number of agents. Recent research ...

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 ...

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

Temporal- difference learning algorithms are central to the domain of reinforcement learning and will be the focus of this paper. Q-learning is one of the most popular TD algorithms [1] . Like many ...

Deep learning algorithms do this via various layers of artificial neural networks which mimic the network of neurons in our brain. ... Difference between deep learning and reinforcement learning .