Portrait de Johan Samir Obando Ceron

Johan Samir Obando Ceron

Doctorat - UdeM
Superviseur⋅e principal⋅e
Aaron Courville
Co-supervisor
Pablo Samuel Castro
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond
Site web
Google Scholar
GitHub

Publications

Bigger, Better, Faster: Human-level Atari with human-level efficiency
Max Schwarzer
Johan Samir Obando Ceron
Aaron Courville
Marc Gendron-Bellemare
Rishabh Agarwal
Pablo Samuel Castro
We introduce a value-based RL agent, which we call BBF, that achieves super-human performance in the Atari 100K benchmark. BBF relies on sca… (voir plus)ling the neural networks used for value estimation, as well as a number of other design choices that enable this scaling in a sample-efficient manner. We conduct extensive analyses of these design choices and provide insights for future work. We end with a discussion about updating the goalposts for sample-efficient RL research on the ALE. We make our code and data publicly available at https://github.com/google-research/google-research/tree/master/bigger_better_faster.
2023-01-01
ICML (publié)
doi.org
openreview.net
The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning
Johan Samir Obando Ceron
Marc Gendron-Bellemare
Pablo Samuel Castro
2023-01-01
Tiny Papers @ ICLR (published)
openreview.net
openreview.net
Variance Double-Down: The Small Batch Size Anomaly in Multistep Deep Reinforcement Learning
Johan Samir Obando Ceron
Marc Gendron-Bellemare
Pablo Samuel Castro
In deep reinforcement learning, multi-step learning is almost unavoidable to achieve state-of-the-art performance. However, the increased va… (voir plus)riance that multistep learning brings makes it difficult to increase the update horizon beyond relatively small numbers. In this paper, we report the counterintuitive finding that decreasing the batch size parameter improves the performance of many standard deep RL agents that use multi-step learning. It is well-known that gradient variance decreases with increasing batch sizes, so obtaining improved performance by increasing variance on two fronts is a rather surprising finding. We conduct a broad set of experiments to better understand what we call the variance doubledown phenomenon.
2022-12-09
NeurIPS.cc/2022/Workshop/DeepRL (inconnu)
openreview.net
openreview.net