Task: Summarization
Input (English): All living organisms struggle against the forces of nature to carve out niches where
 they can maintain relative stasis. We propose that such a search for order amidst
 chaos might offer a unifying principle for the emergence of useful behaviors in
 artificial agents. We formalize this idea into an unsupervised reinforcement learning
 method called surprise minimizing RL (SMiRL). SMiRL trains an agent with the
 objective of maximizing the probability of observed states under a model trained on
 all previously seen states. The resulting agents acquire several proactive behaviors
 to seek and maintain stable states such as balancing and damage avoidance, that
 are closely tied to the affordances of the environment and its prevailing sources
 of entropy, such as winds, earthquakes, and other agents.  We demonstrate that
 our surprise minimizing agents can successfully play Tetris, Doom, and control
 a  humanoid  to  avoid  falls,  without  any  task-specific  reward  supervision.    We
further show that SMiRL can be used as an unsupervised pre-training objective
 that substantially accelerates subsequent reward-driven learning
Output (English): Learning emergent behavior by minimizing Bayesian surprise with RL in natural environments with entropy.