  It is well known that reinforcement learning can be cast as inference in an
appropriate probabilistic model. However, this commonly involves introducing a
distribution over agent trajectories with probabilities proportional to
exponentiated rewards. In this work, we formulate reinforcement learning as
Bayesian inference without resorting to rewards, and show that rewards are
derived from agent's preferences, rather than the other way around. We argue
that agent preferences should be specified stochastically rather than
deterministically. Reinforcement learning via inference with stochastic
preferences naturally describes agent behaviors, does not require introducing
rewards and exponential weighing of trajectories, and allows to reason about
agents using the solid foundation of Bayesian statistics. Stochastic
conditioning, a probabilistic programming paradigm for conditioning models on
distributions rather than values, is the formalism behind agents with
probabilistic preferences. We demonstrate realization of our approach on case
studies using both a two-agent coordinate game and a single agent acting in a
noisy environment, showing that despite superficial differences, both cases can
be modeled and reasoned about based on the same principles.
