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It is well known that reinforcement learning can be cast as inference in anappropriate probabilistic model. However, this commonly involves introducing adistribution over agent trajectories with probabilities proportional toexponentiated rewards. In this work, we formulate reinforcement learning asBayesian inference without resorting to rewards, and show that rewards arederived from agent's preferences, rather than the other way around. We arguethat agent preferences should be specified stochastically rather thandeterministically. Reinforcement learning via inference with stochasticpreferences naturally describes agent behaviors, does not require introducingrewards and exponential weighing of trajectories, and allows to reason aboutagents using the solid foundation of Bayesian statistics. Stochasticconditioning, a probabilistic programming paradigm for conditioning models ondistributions rather than values, is the formalism behind agents withprobabilistic preferences. We demonstrate realization of our approach on casestudies using both a two-agent coordinate game and a single agent acting in anoisy environment, showing that despite superficial differences, both cases canbe modeled and reasoned about based on the same principles.

Bob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs