Błażej Osiński | DeepAI

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Featured Co-authors

Sergey Levine
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∙ 07/10/2022

LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action

Goal-conditioned policies for robotic navigation can be trained on large...

0 Dhruv Shah, et al. ∙

∙ 11/22/2021

Off-Policy Correction For Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) provides a framework for probl...

0 Michał Zawalski, et al. ∙

∙ 09/28/2021

SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies

In this paper we present the first safe system for full control of self-...

0 Matt Vitelli, et al. ∙

∙ 09/27/2021

Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients

In this work we are the first to present an offline policy gradient meth...

0 Oliver Scheel, et al. ∙

∙ 05/26/2021

What data do we need for training an AV motion planner?

We investigate what grade of sensor data is required for training an imi...

2 Long Chen, et al. ∙

∙ 05/26/2021

SimNet: Learning Reactive Self-driving Simulations from Real-world Observations

In this work, we present a simple end-to-end trainable machine learning ...

12 Luca Bergamini, et al. ∙

∙ 12/16/2020

CARLA Real Traffic Scenarios – novel training ground and benchmark for autonomous driving

This work introduces interactive traffic scenarios in the CARLA simulato...

0 Błażej Osiński, et al. ∙

∙ 11/29/2019

Simulation-based reinforcement learning for real-world autonomous driving

We use synthetic data and a reinforcement learning algorithm to train a ...

0 Błażej Osiński, et al. ∙

∙ 03/01/2019

Model-Based Reinforcement Learning for Atari

Model-free reinforcement learning (RL) can be used to learn effective po...

20 Łukasz Kaiser, et al. ∙

∙ 04/02/2018

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked wit...

0 Łukasz Kidziński, et al. ∙