  When developing reinforcement learning agents, the standard approach is to
train an agent to converge to a fixed policy that is as close to optimal as
possible for a single fixed reward function. If different agent behaviour is
required in the future, an agent trained in this way must normally be either
fully or partially retrained, wasting valuable time and resources. In this
study, we leverage multi-objective reinforcement learning to create tunable
agents, i.e. agents that can adopt a range of different behaviours according to
the designer's preferences, without the need for retraining. We apply this
technique to sequential social dilemmas, settings where there is inherent
tension between individual and collective rationality. Learning a single fixed
policy in such settings leaves one at a significant disadvantage if the
opponents' strategies change after learning is complete. In our work, we
demonstrate empirically that the tunable agents framework allows easy adaption
between cooperative and competitive behaviours in sequential social dilemmas
without the need for retraining, allowing a single trained agent model to be
adjusted to cater for a wide range of behaviours and opponent strategies.
