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Workshop: Reincarnating Reinforcement Learning

Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models

Xingyuan Zhang · Philip Becker-Ehmck · Patrick van der Smagt · Maximilian Karl


Unlike most reinforcement learning agents which require an unrealistic amount of environment interactions to learn a new behaviour, humans excel at learning quickly by merely observing and imitating others. This ability highly depends on the fact that humans have a model of their own embodiment that allows them to infer the most likely actions that led to the observed behaviour. In this paper, we propose Action Inference by Maximising Evidence (AIME) to replicate this behaviour using world models. AIME consists of two distinct phases. In the first phase, the agent learns a world model from its past experience to understand its own body by maximising the evidence lower bound (ELBO). While in the second phase, the agent is given some observation-only demonstrations of an expert performing a novel task and tries to imitate the expert's behaviour. AIME achieves this by defining a policy as an inference model and maximising the evidence of the demonstration under the policy and world model. Our method is "zero-shot" in the sense that it does not require further interactions with the environment after given the demonstration. We empirically validate the zero-shot imitation performance of our method on the Walker of the DeepMind Control Suite and find it outperforms the state-of-the-art baselines. We also find AIME with image observations still matches the performance of the baseline observing the true low-dimensional state of the environment.

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