PyTorch 1.x Reinforcement Learning Cookbook

How it works...

In Step 2, we calculated the transition probability after k steps, which is the k^th power of the transition matrix. You will see the following output:

>>> print("Transition probability after 2 steps:\n{}".format(T_2))Transition probability after 2 steps:tensor([[0.6400, 0.3600],        [0.4800, 0.5200]])>>> print("Transition probability after 5 steps:\n{}".format(T_5))Transition probability after 5 steps:tensor([[0.5670, 0.4330],        [0.5773, 0.4227]])>>> print("Transition probability after 10 steps:\n{}".format(T_10))Transition probability after 10 steps:tensor([[0.5715, 0.4285],        [0.5714, 0.4286]])>>> print("Transition probability after 15 steps:\n{}".format(T_15))Transition probability after 15 steps:tensor([[0.5714, 0.4286], [0.5714, ...