Reviews: Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior

The paper investigates the problem of inferring an agent's belief of the system dynamics of an MDP, given demonstrations of its behavior and the reward function it was optimizing. Knowledge of this internal belief can be used for Inverse Reinforcement Learning of an unknown task in the same environment. Furthermore, given the action provided by the agent, its intended action on the true dynamics can be inferred. This allows for assistive tele-operation, by applying the intended actions to the system instead of the provided ones. The proposed method models the agent using the model derived in maximum causal entropy inverse reinforcement learning.