  Many applications of imitation learning require the agent to generate the
full distribution of behaviour observed in the training data. For example, to
evaluate the safety of autonomous vehicles in simulation, accurate and diverse
behaviour models of other road users are paramount. Existing methods that
improve this distributional realism typically rely on hierarchical policies.
These condition the policy on types such as goals or personas that give rise to
multi-modal behaviour. However, such methods are often inappropriate for
stochastic environments where the agent must also react to external factors:
because agent types are inferred from the observed future trajectory during
training, these environments require that the contributions of internal and
external factors to the agent behaviour are disentangled and only internal
factors, i.e., those under the agent's control, are encoded in the type.
Encoding future information about external factors leads to inappropriate agent
reactions during testing, when the future is unknown and types must be drawn
independently from the actual future. We formalize this challenge as
distribution shift in the conditional distribution of agent types under
environmental stochasticity. We propose Robust Type Conditioning (RTC), which
eliminates this shift with adversarial training under randomly sampled types.
Experiments on two domains, including the large-scale Waymo Open Motion
Dataset, show improved distributional realism while maintaining or improving
task performance compared to state-of-the-art baselines.
