## General Framework Extends T-REX (see [summary](https://www.shortscience.org/paper?bibtexKey=journals/corr/1904.06387&a=muntermulehitch)) so that preferences (rankings) over demonstrations are generated automatically (back to the common IL/IRL setting where we only have access to a set of unlabeled demonstrations). Also derives some theoretical requirements and guarantees for better-than-demonstrator performance. ## Motivations * Preferences over demonstrations may be difficult to obtain in practice. * There is no theoretical understanding of the requirements that lead to outperforming demonstrator. ## Contributions * Theoretical results (with linear reward function) on when better-than-demonstrator performance is possible: 1- the demonstrator must be suboptimal (room for improvement, obviously), 2- the learned reward must be close enough to the reward that the demonstrator is suboptimally optimizing for (be able to accurately capture the intent of the demonstrator), 3- the learned policy (optimal wrt the learned reward) must be close enough to the optimal policy (wrt to the ground truth reward). Obviously if we have 2- and a good enough RL algorithm we should have 3-, so it might be interesting to see if one can derive a requirement from only 1- and 2- (and possibly a good enough RL algo). * Theoretical results (with linear reward function) showing that pairwise preferences over demonstrations reduce the error and ambiguity of the reward learning. They show that without rankings two policies might have equal performance under a learned reward (that makes expert's demonstrations optimal) but very different performance under the true reward (that makes the expert optimal everywhere). Indeed, the expert's demonstration may reveal very little information about the reward of (suboptimal or not) unseen regions which may hurt very much the generalizations (even with RL as it would try to generalize to new states under a totally wrong reward). They also show that pairwise preferences over trajectories effectively give half-space constraints on the feasible reward function domain and thus may decrease exponentially the reward function ambiguity. * Propose a practical way to generate as many ranked demos as desired. ## Additional Assumption Very mild, assumes that a Behavioral Cloning (BC) policy trained on the provided demonstrations is better than a uniform random policy. ## Disturbance-based Reward Extrapolation (D-REX) !(https://i.imgur.com/9g6tOrF.png) !(https://i.imgur.com/zSRlDcr.png) They also show that the more noise added to the BC policy the lower the performance of the generated trajs. ## Results Pretty much like T-REX.