Reinforcement Learning, Machine Learning, Policy, Data Science, Artificial Intelligence.
of which includes asynchronous updates during the policy evaluation step. Since the majority of reinforcement algorithms require a lot of computational resources, this technique becomes very useful and allows efficiently trading accuracy for gains in speed. The policy iteration algorithm we have