Hands-On Q-Learning with Python

Alpha – deterministic versus stochastic environments

Your agent's learning rate alpha ranges from zero to one. Setting the learning rate to zero will cause your agent to learn nothing. All of its exploration of its environment and the rewards it receives will not affect its behavior at all, and it will continue to behave completely randomly.

Setting the learning rate to one will cause your agent to learn policies that are fully specific to a deterministic environment. One important distinction to understand is between deterministic and stochastic environments and policies.

Briefly, in a deterministic environment, the output is totally determined by the initial conditions and there is no randomness involved. We always take the same action ...