Search Results for author: Anton Raichuk

Found 8 papers, 4 papers with code

Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation

1 code implementation24 Jun 2021 C. Daniel Freeman, Erik Frey, Anton Raichuk, Sertan Girgin, Igor Mordatch, Olivier Bachem

We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.

OpenAI Gym

What Matters for Adversarial Imitation Learning?

no code implementations1 Jun 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

Agent-Centric Representations for Multi-Agent Reinforcement Learning

no code implementations19 Apr 2021 Wenling Shang, Lasse Espeholt, Anton Raichuk, Tim Salimans

Empirically, agent-centric representation learning leads to the emergence of more complex cooperation strategies between agents as well as enhanced sample efficiency and generalization.

Multi-agent Reinforcement Learning Relational Reasoning +1

Google Research Football: A Novel Reinforcement Learning Environment

1 code implementation25 Jul 2019 Karol Kurach, Anton Raichuk, Piotr Stańczyk, Michał Zając, Olivier Bachem, Lasse Espeholt, Carlos Riquelme, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly

Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.

Game of Football

Episodic Curiosity through Reachability

1 code implementation ICLR 2019 Nikolay Savinov, Anton Raichuk, Raphaël Marinier, Damien Vincent, Marc Pollefeys, Timothy Lillicrap, Sylvain Gelly

One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning.

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