  Multi-objective reinforcement learning (MORL) algorithms extend conventional
reinforcement learning (RL) to the more general case of problems with multiple,
conflicting objectives, represented by vector-valued rewards. Widely-used
scalar RL methods such as Q-learning can be modified to handle multiple
objectives by (1) learning vector-valued value functions, and (2) performing
action selection using a scalarisation or ordering operator which reflects the
user's utility with respect to the different objectives. However, as we
demonstrate here, if the user's utility function maps widely varying
vector-values to similar levels of utility, this can lead to interference in
the value-function learned by the agent, leading to convergence to sub-optimal
policies. This will be most prevalent in stochastic environments when
optimising for the Expected Scalarised Return criterion, but we present a
simple example showing that interference can also arise in deterministic
environments. We demonstrate empirically that avoiding the use of random
tie-breaking when identifying greedy actions can ameliorate, but not fully
overcome, the problems caused by value function interference.
