62 citations · 149 across the 19 of their papers we have counts for
5 papers · 1 filter
Structured Policy Representation: Imposing Stability in arbitrarily conditioned dynamic systems
Julen Urain, Davide Tateo, Tianyu Ren +1
We present a new family of deep neural network-based dynamic systems. The presented dynamics are globally stable and can be conditioned with an arbitrary context state. We show how…
ImitationFlow: Learning Deep Stable Stochastic Dynamic Systems by Normalizing Flows
Julen Urain, Michelle Ginesi, Davide Tateo +1
We introduce ImitationFlow, a novel Deep generative model that allows learning complex globally stable, stochastic, nonlinear dynamics. Our approach extends the Normalizing Flows f…
Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts
Riad Akrour, Davide Tateo, Jan Peters
Reinforcement learning (RL) has demonstrated its ability to solve high dimensional tasks by leveraging non-linear function approximators. However, these successes are mostly achiev…
MushroomRL: Simplifying Reinforcement Learning Research
Carlo D'Eramo, Davide Tateo, Andrea Bonarini +2
MushroomRL is an open-source Python library developed to simplify the process of implementing and running Reinforcement Learning (RL) experiments. Compared to other available libra…
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning
Simone Parisi, Davide Tateo, Maximilian Hensel +3
Reinforcement learning with sparse rewards is still an open challenge. Classic methods rely on getting feedback via extrinsic rewards to train the agent, and in situations where th…