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Evgenii Nikishin

evgenii.nikishin@mila.quebec
PhD - Université de Montréal
Supervisor
Pierre-Luc Bacon
Co-supervisor
Aaron Courville
Website
Github
Google Scholar

Publications

Maxwell's Demon at Work: Efficient Pruning by Leveraging Saturation of Neurons
Simon Dufort-Labbé
Pierluca D'Oro
Evgenii Nikishin
Razvan Pascanu
Pierre-Luc Bacon
Aristide Baratin
2024-03-12
ArXiv (preprint)
doi.org
arxiv.org
The Curse of Diversity in Ensemble-Based Exploration
Zhixuan Lin
Pierluca D'Oro
Evgenii Nikishin
Aaron Courville
We uncover a surprising phenomenon in deep reinforcement learning: training a diverse ensemble of data-sharing agents -- a well-established … (see more)exploration strategy -- can significantly impair the performance of the individual ensemble members when compared to standard single-agent training. Through careful analysis, we attribute the degradation in performance to the low proportion of self-generated data in the shared training data for each ensemble member, as well as the inefficiency of the individual ensemble members to learn from such highly off-policy data. We thus name this phenomenon *the curse of diversity*. We find that several intuitive solutions -- such as a larger replay buffer or a smaller ensemble size -- either fail to consistently mitigate the performance loss or undermine the advantages of ensembling. Finally, we demonstrate the potential of representation learning to counteract the curse of diversity with a novel method named Cross-Ensemble Representation Learning (CERL) in both discrete and continuous control domains. Our work offers valuable insights into an unexpected pitfall in ensemble-based exploration and raises important caveats for future applications of similar approaches.
2024-01-16
ICLR.cc/2024/Conference (poster)
openreview.net
openreview.net
Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier
Pierluca D'Oro
Max Schwarzer
Evgenii Nikishin
Pierre-Luc Bacon
Marc Gendron-Bellemare
Aaron Courville
Increasing the replay ratio, the number of updates of an agent's parameters per environment interaction, is an appealing strategy for improv… (see more)ing the sample efficiency of deep reinforcement learning algorithms. In this work, we show that fully or partially resetting the parameters of deep reinforcement learning agents causes better replay ratio scaling capabilities to emerge. We push the limits of the sample efficiency of carefully-modified algorithms by training them using an order of magnitude more updates than usual, significantly improving their performance in the Atari 100k and DeepMind Control Suite benchmarks. We then provide an analysis of the design choices required for favorable replay ratio scaling to be possible and discuss inherent limits and tradeoffs.
2023-02-01
ICLR.cc/2023/Conference (notable)
openreview.net
openreview.net