  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. In particular, in domains where states by nature only have an
ordinal ranking and where meaningful distance information between game state
values are not available, a numerical reward signal is necessarily biased. In
this paper, we take a look at Monte Carlo Tree Search (MCTS), a popular
algorithm to solve MDPs, highlight a reoccurring problem concerning its use of
rewards, and show that an ordinal treatment of the rewards overcomes this
problem. Using the General Video Game Playing framework we show a dominance of
our newly proposed ordinal MCTS algorithm over preference-based MCTS, vanilla
MCTS and various other MCTS variants.
