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Many applications of imitation learning require the agent to generate thefull distribution of behaviour observed in the training data. For example, toevaluate the safety of autonomous vehicles in simulation, accurate and diversebehaviour models of other road users are paramount. Existing methods thatimprove this distributional realism typically rely on hierarchical policies.These condition the policy on types such as goals or personas that give rise tomulti-modal behaviour. However, such methods are often inappropriate forstochastic environments where the agent must also react to external factors:because agent types are inferred from the observed future trajectory duringtraining, these environments require that the contributions of internal andexternal factors to the agent behaviour are disentangled and only internalfactors, i.e., those under the agent's control, are encoded in the type.Encoding future information about external factors leads to inappropriate agentreactions during testing, when the future is unknown and types must be drawnindependently from the actual future. We formalize this challenge asdistribution shift in the conditional distribution of agent types underenvironmental stochasticity. We propose Robust Type Conditioning (RTC), whicheliminates this shift with adversarial training under randomly sampled types.Experiments on two domains, including the large-scale Waymo Open MotionDataset, show improved distributional realism while maintaining or improvingtask performance compared to state-of-the-art baselines.

Hierarchical Imitation Learning for Stochastic Environments