Search Results for author: Steven Kapturowski

Found 10 papers, 4 papers with code

Unlocking the Power of Representations in Long-term Novelty-based Exploration

no code implementations2 May 2023 Alaa Saade, Steven Kapturowski, Daniele Calandriello, Charles Blundell, Pablo Sprechmann, Leopoldo Sarra, Oliver Groth, Michal Valko, Bilal Piot

We introduce Robust Exploration via Clustering-based Online Density Estimation (RECODE), a non-parametric method for novelty-based exploration that estimates visitation counts for clusters of states based on their similarity in a chosen embedding space.

Atari Games Density Estimation

Human-level Atari 200x faster

no code implementations15 Sep 2022 Steven Kapturowski, Víctor Campos, Ray Jiang, Nemanja Rakićević, Hado van Hasselt, Charles Blundell, Adrià Puigdomènech Badia

The task of building general agents that perform well over a wide range of tasks has been an importantgoal in reinforcement learning since its inception.

Revisiting Peng's Q($λ$) for Modern Reinforcement Learning

no code implementations27 Feb 2021 Tadashi Kozuno, Yunhao Tang, Mark Rowland, Rémi Munos, Steven Kapturowski, Will Dabney, Michal Valko, David Abel

These results indicate that Peng's Q($\lambda$), which was thought to be unsafe, is a theoretically-sound and practically effective algorithm.

Continuous Control reinforcement-learning +1

Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning

no code implementations24 Feb 2021 Víctor Campos, Pablo Sprechmann, Steven Hansen, Andre Barreto, Steven Kapturowski, Alex Vitvitskyi, Adrià Puigdomènech Badia, Charles Blundell

We introduce Behavior Transfer (BT), a technique that leverages pre-trained policies for exploration and that is complementary to transferring neural network weights.

reinforcement-learning Reinforcement Learning (RL) +1

Temporal Difference Uncertainties as a Signal for Exploration

no code implementations5 Oct 2020 Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, Andre Barreto, Razvan Pascanu

Instead, we incorporate it as an intrinsic reward and treat exploration as a separate learning problem, induced by the agent's temporal difference uncertainties.

Never Give Up: Learning Directed Exploration Strategies

3 code implementations ICLR 2020 Adrià Puigdomènech Badia, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Bilal Piot, Steven Kapturowski, Olivier Tieleman, Martín Arjovsky, Alexander Pritzel, Andew Bolt, Charles Blundell

Our method doubles the performance of the base agent in all hard exploration in the Atari-57 suite while maintaining a very high score across the remaining games, obtaining a median human normalised score of 1344. 0%.

Atari Games

Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

1 code implementation ICLR 2020 Tom Le Paine, Caglar Gulcehre, Bobak Shahriari, Misha Denil, Matt Hoffman, Hubert Soyer, Richard Tanburn, Steven Kapturowski, Neil Rabinowitz, Duncan Williams, Gabriel Barth-Maron, Ziyu Wang, Nando de Freitas, Worlds Team

This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions.

Recurrent Experience Replay in Distributed Reinforcement Learning

3 code implementations ICLR 2019 Steven Kapturowski, Georg Ostrovski, Will Dabney, John Quan, Remi Munos

Using a single network architecture and fixed set of hyperparameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and surpasses the state of the art on DMLab-30.

Atari Games reinforcement-learning +1

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