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Partially Observable Markov Decision Processes (POMDPs) are used to modelenvironments where the full state cannot be perceived by an agent. As such theagent needs to reason taking into account the past observations and actions.However, simply remembering the full history is generally intractable due tothe exponential growth in the history space. Maintaining a probabilitydistribution that models the belief over what the true state is can be used asa sufficient statistic of the history, but its computation requires access tothe model of the environment and is often intractable. While SOTA algorithmsuse Recurrent Neural Networks to compress the observation-action history aimingto learn a sufficient statistic, they lack guarantees of success and can leadto sub-optimal policies. To overcome this, we propose the Wasserstein BeliefUpdater, an RL algorithm that learns a latent model of the POMDP and anapproximation of the belief update. Our approach comes with theoreticalguarantees on the quality of our approximation ensuring that our outputtedbeliefs allow for learning the optimal value function.

The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models