  In recent years, work has been done to develop the theory of General
Reinforcement Learning (GRL). However, there are few examples demonstrating
these results in a concrete way. In particular, there are no examples
demonstrating the known results regarding gener- alised discounting. We have
added to the GRL simulation platform AIXIjs the functionality to assign an
agent arbitrary discount functions, and an environment which can be used to
determine the effect of discounting on an agent's policy. Using this, we
investigate how geometric, hyperbolic and power discounting affect an informed
agent in a simple MDP. We experimentally reproduce a number of theoretical
results, and discuss some related subtleties. It was found that the agent's
behaviour followed what is expected theoretically, assuming appropriate
parameters were chosen for the Monte-Carlo Tree Search (MCTS) planning
algorithm.
