Search Results for author: Robert Dadashi

Found 15 papers, 3 papers with code

Get Back Here: Robust Imitation by Return-to-Distribution Planning

no code implementations2 May 2023 Geoffrey Cideron, Baruch Tabanpour, Sebastian Curi, Sertan Girgin, Leonard Hussenot, Gabriel Dulac-Arnold, Matthieu Geist, Olivier Pietquin, Robert Dadashi

We consider the Imitation Learning (IL) setup where expert data are not collected on the actual deployment environment but on a different version.

Imitation Learning

Learning Energy Networks with Generalized Fenchel-Young Losses

no code implementations19 May 2022 Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist

To learn the parameters of the energy function, the solution to that optimization problem is typically fed into a loss function.

Imitation Learning

What Matters for Adversarial Imitation Learning?

no code implementations NeurIPS 2021 Manu Orsini, Anton Raichuk, Léonard Hussenot, Damien Vincent, Robert Dadashi, Sertan Girgin, Matthieu Geist, Olivier Bachem, Olivier Pietquin, Marcin Andrychowicz

To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.

Continuous Control Imitation Learning

Offline Reinforcement Learning with Pseudometric Learning

no code implementations ICLR Workshop SSL-RL 2021 Robert Dadashi, Shideh Rezaeifar, Nino Vieillard, Léonard Hussenot, Olivier Pietquin, Matthieu Geist

In the presence of function approximation, and under the assumption of limited coverage of the state-action space of the environment, it is necessary to enforce the policy to visit state-action pairs close to the support of logged transitions.

reinforcement-learning Reinforcement Learning (RL)

Show me the Way: Intrinsic Motivation from Demonstrations

no code implementations23 Jun 2020 Léonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin

Using an inverse RL approach, we show that complex exploration behaviors, reflecting different motivations, can be learnt and efficiently used by RL agents to solve tasks for which exhaustive exploration is prohibitive.

Decision Making Experimental Design +1

Statistics and Samples in Distributional Reinforcement Learning

no code implementations21 Feb 2019 Mark Rowland, Robert Dadashi, Saurabh Kumar, Rémi Munos, Marc G. Bellemare, Will Dabney

We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distribution.

Distributional Reinforcement Learning reinforcement-learning +1

The Value Function Polytope in Reinforcement Learning

no code implementations31 Jan 2019 Robert Dadashi, Adrien Ali Taïga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare

We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes.

reinforcement-learning Reinforcement Learning (RL)

Boosting Model Performance through Differentially Private Model Aggregation

no code implementations12 Nov 2018 Sophia Collet, Robert Dadashi, Zahi N. Karam, Chang Liu, Parinaz Sobhani, Yevgeniy Vahlis, Ji Chao Zhang

In this work, two approaches for private model aggregation are proposed that enable the transfer of knowledge from existing models trained on other companies' datasets to a new company with limited labeled data while protecting each client company's underlying individual sensitive information.

Cannot find the paper you are looking for? You can Submit a new open access paper.