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Found 6 papers, 2 papers with code
Synthesis of Hierarchical Controllers Based on Deep Reinforcement Learning Policies
We propose a novel approach to the problem of controller design for environments modeled as Markov decision processes (MDPs).
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees
Our approach yields bisimulation guarantees while learning the distilled policy, allowing concrete optimization of the abstraction and representation model quality.
The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models
Maintaining a probability distribution that models the belief over what the true state is can be used as a sufficient statistic of the history, but its computation requires access to the model of the environment and is often intractable.
Distillation of RL Policies with Formal Guarantees via Variational Abstraction of Markov Decision Processes (Technical Report)
Finally, we show how one can use a policy obtained via state-of-the-art RL to efficiently train a variational autoencoder that yields a discrete latent model with provably approximately correct bisimulation guarantees.
Simple Strategies in Multi-Objective MDPs (Technical Report)
That is, strategies that are pure (no randomization) and have bounded memory.
Life is Random, Time is Not: Markov Decision Processes with Window Objectives
The window mechanism was introduced by Chatterjee et al. to strengthen classical game objectives with time bounds.
