Hands-On Reinforcement Learning with Python

The softmax exploration algorithm

Softmax exploration, also known as Boltzmann exploration, is another strategy used for finding an optimal bandit. In the epsilon-greedy policy, we consider all of the non-best arms equivalently, but in softmax exploration, we select an arm based on a probability from the Boltzmann distribution. The probability of selecting an arm is given by:

is called a temperature factor, which specifies how many random arms we can explore. When is high, all arms will be explored equally, but when is low, high-rewarding ...