Portrait de Ivan Anokhin n'est pas disponible

Ivan Anokhin

Doctorat - UdeM
Superviseur⋅e principal⋅e
Irina Rish
Co-supervisor
Samira Ebrahimi Kahou
Sujets de recherche
Apprentissage par renforcement
Google Scholar
GitHub

Publications

Learning From the Past with Cascading Eligibility Traces
Tokiniaina Raharison Ralambomihanta
Ivan Anokhin
Roman Pogodin
S Ebrahimi Kahou
Jonathan Cornford
Blake Aaron Richards
2025-06-16
ArXiv (prépublication)
arxiv.org
arxiv.org
AIF-GEN: Open-Source Platform and Synthetic Dataset Suite for Reinforcement Learning on Large Language Models
Jacob Chmura
Shahrad Mohammadzadeh
Ivan Anokhin
Jacob-Junqi Tian
Mandana Samiei
Taz Scott-Talib
Irina Rish
Doina Precup
Reihaneh Rabbany
Nishanth Anand
2025-06-08
CODEML @ International Conference on Machine Learning (publié)
openreview.net
openreview.net
Handling Delay in Reinforcement Learning Caused by Parallel Computations of Neurons
Ivan Anokhin
Rishav
Stephen Chung
Irina Rish
S Ebrahimi Kahou
Biological neural networks operate in parallel, a feature that sets them apart from artificial neural networks and can significantly enhance… (voir plus) inference speed. However, this parallelism introduces challenges: when each neuron operates asynchronously with a fixed execution time, an
2024-06-18
ICML.cc/2024/Workshop/ARLET (poster)
openreview.net
openreview.net
Thinker: Learning to Plan and Act
Stephen Chung
Ivan Anokhin
David Krueger
We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a le… (voir plus)arned world model. The Thinker algorithm wraps the environment with a world model and introduces new actions designed for interacting with the world model. These model-interaction actions enable agents to perform planning by proposing alternative plans to the world model before selecting a final action to execute in the environment. This approach eliminates the need for handcrafted planning algorithms by enabling the agent to learn how to plan autonomously and allows for easy interpretation of the agent's plan with visualization. We demonstrate the algorithm's effectiveness through experimental results in the game of Sokoban and the Atari 2600 benchmark, where the Thinker algorithm achieves state-of-the-art performance and competitive results, respectively. Visualizations of agents trained with the Thinker algorithm demonstrate that they have learned to plan effectively with the world model to select better actions. Thinker is the first work showing that an RL agent can learn to plan with a learned world model in complex environments.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net