Ordinal Monte Carlo Tree Search

Understand

In many problem settings, most notably in game playing, an agent receives a possibly delayed reward for its actions.

• Often, those rewards are handcrafted and not naturally given.
• Even simple terminal-only rewards, like winning equals 1 and losing equals -1, can not be seen as an unbiased statement, since these values are chosen arbitrarily, and the behavior of the learner may change with different encodings, such as setting the value of a loss to -0:5, which is often done in practice to encourage learning.
• It is hard to argue about good rewards and the performance of an agent often depends on the design of the reward signal.