Hands-On Reinforcement Learning for Games

Exploration versus exploitation

As we have seen, having our agent always make the best choice limits their ability to learn the full values of a single state never mind multiple connected states. This also severely limits an agent's ability to learn, especially in environments where multiple states converge and diverge. What we need, therefore, is a way for our agent to choose an action based on a policy that favors more equal action/value distribution. Essentially, we need a policy that allows our agent to explore as well as exploit its knowledge to maximize learning. There are multiple variations and ways of balancing the trade-off between exploration and exploitation. Much of this will depend on the particular environment as well as the ...