Ivan Anokhin

Co-supervisor

Real-time Reinforcement Learning

Research Topics

Reinforcement Learning

Google Scholar

GitHub

Blog Posts

Deux robots dans une cuisine, en train de préparer le dîner. L'un coupe les légumes et l'autre fait une omelette.

June 20, 2025

Ivan Anokhin

Matthew Riemer

Rishav Rishav

Gopeshh Subbaraj

Glen Berseth

Read the article

Publications

AIF-GEN: Open-Source Platform and Synthetic Dataset Suite for Reinforcement Learning on Large Language Models

Jacob Chmura

Shahrad Mohammadzadeh

Taz Scott-Talib

Nishanth Anand

2025-06-09

ICML.cc/2025/Workshop/CODEML (published)

Handling Delay in Real-Time Reinforcement Learning

Rishav

Matthew D Riemer

Stephen Chung

Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second… (see more) due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of

2025-01-22

ICLR.cc/2025/Conference (poster)

Handling Delay in Real-Time Reinforcement Learning

Rishav

Matthew D Riemer

Stephen Chung

2025-01-22

ICLR.cc/2025/Conference (poster)

doi.org

Handling Delay in Reinforcement Learning Caused by Parallel Computations of Neurons

Rishav

Stephen Chung

Biological neural networks operate in parallel, a feature that sets them apart from artificial neural networks and can significantly enhance… (see more) inference speed. However, this parallelism introduces challenges: when each neuron operates asynchronously with a fixed execution time, an

2024-06-19

ICML.cc/2024/Workshop/ARLET (poster)

Thinker: Learning to Plan and Act

Stephen Chung

David Scott Krueger

We propose the Thinker algorithm, a novel approach that enables reinforcement learning agents to autonomously interact with and utilize a le… (see more)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.