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Reinforcement learning methods require careful design involving a rewardfunction to obtain the desired action policy for a given task. In the absenceof hand-crafted reward functions, prior work on the topic has proposed severalmethods for reward estimation by using expert state trajectories and actionpairs. However, there are cases where complete or good action informationcannot be obtained from expert demonstrations. We propose a novel reinforcementlearning method in which the agent learns an internal model of observation onthe basis of expert-demonstrated state trajectories to estimate rewards withoutcompletely learning the dynamics of the external environment from state-actionpairs. The internal model is obtained in the form of a predictive model for thegiven expert state distribution. During reinforcement learning, the agentpredicts the reward as a function of the difference between the actual stateand the state predicted by the internal model. We conducted multipleexperiments in environments of varying complexity, including the Super MarioBros and Flappy Bird games. We show our method successfully trains goodpolicies directly from expert game-play videos.

Internal Model from Observations for Reward Shaping