Before revision:  We make these properties explicit by showing that the active inference agent's ability to carry out online planning, in a pure-belief setting, enables it to act optimally, given the non-stationary dynamics of an environment when compared to both Q-learning and Bayesian model-based reinforcement learning agents .
After revision:  We make these properties explicit by showing that the active inference agent's ability to carry out online planning, in a pure-belief setting, enables it to act optimally, given the non-stationary dynamics of an environment when compared to both Q-learning and Bayesian model-based RL agents; by placing zero prior preferences over rewards and by learning the prior preferences over the observations corresponding to reward .