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Talent Scout

N

Nathan S. de Lara

University of Toronto

F

Florian Shkurti

University of Toronto

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References (52)

[1]
What Really Matters in Matrix-Whitening Optimizers?
2025Kevin Frans, Pieter Abbeel et al.
[2]
EXPO: Stable Reinforcement Learning with Expressive Policies
2025Perry Dong, Qiyang Li et al.
[3]
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
2025Mustafa Shukor, Dana Aubakirova et al.
[4]
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
2025Nvidia, Johan Bjorck et al.
[5]
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data
2024Zhiyuan Zhou, Andy Peng et al.
[6]
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
2024Hojoon Lee, Dongyoon Hwang et al.
[7]
CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making
2024Zibin Dong, Yifu Yuan et al.
[8]
A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning
2023Yinmin Zhang, Jie Liu et al.
[9]
Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
2023Xiaoyu Wen, Xudong Yu et al.
[10]
Actor-Critic Alignment for Offline-to-Online Reinforcement Learning
2023Zishun Yu, Xinhua Zhang
[11]
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning
2023Siyuan Guo, Yanchao Sun et al.
[12]
ENOTO: Improving Offline-to-Online Reinforcement Learning with Q-Ensembles
2023Kai-Wen Zhao, Jianye Hao et al.
[13]
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
2023Jianxiong Li, Xiao Hu et al.
[14]
IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
2023Philippe Hansen-Estruch, Ilya Kostrikov et al.
[15]
Adaptive Policy Learning for Offline-to-Online Reinforcement Learning
2023Han Zheng, Xufang Luo et al.
[16]
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
2023Mitsuhiko Nakamoto, Yuexiang Zhai et al.
[17]
Aligning Human and Robot Representations
2023Andreea Bobu, Andi Peng et al.
[18]
Policy Expansion for Bridging Offline-to-Online Reinforcement Learning
2023Haichao Zhang, Weiwen Xu et al.
[19]
Is Conditional Generative Modeling all you need for Decision-Making?
2022Anurag Ajay, Yilun Du et al.
[20]
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
2022Hong Liu, Sang Michael Xie et al.

Showing 20 of 52 references

Founder's Pitch

"Optimize actor-critics to seamlessly transition from offline pre-training to online fine-tuning without performance drops."

Reinforcement LearningScore: 5View PDF ↗

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0-10 scale

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2.5

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2/4 signals

5

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3/4 signals

7.5

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Why It Matters

This research addresses the critical challenge of ensuring offline reinforcement learning models can be fine-tuned online without performance loss, which is key for efficient real-world applications in rapidly changing environments.

Product Angle

Commercialize SMAC as a software tool or library that integrates with existing RL frameworks, targeting industries that rely on continuous model updates and fine-tuning, such as logistics and manufacturing robotics.

Disruption

SMAC could replace existing RL solutions that require extensive retraining after offline pre-training, offering a more efficient and performance-stable integration in real-world applications.

Product Opportunity

The market for machine learning in industrial automation is growing rapidly, and this technique could save significant costs for companies by improving RL model adaptability and reducing the cycle time for implementing model updates.

Use Case Idea

Develop an RL-based platform for autonomous systems where models pre-trained on past data can be fine-tuned online in new environments without a drop in performance, crucial for sectors like robotics and autonomous vehicles.

Science

SMAC is an offline RL method that applies a regularization technique to the Q-function during the offline phase, ensuring that the actor-critic model transitions seamlessly to online scenarios without encountering performance dips. This involves aligning action gradients with policy score derivatives, facilitating optimization paths that avoid low-performance valleys in the parameter space.

Method & Eval

The method was tested by applying SMAC to several benchmark tasks in the D4RL suite, where it improved regret measures by 34-58% over baselines, demonstrating smooth performance transition from offline to online RL algorithms compared to state-of-the-art methods.

Caveats

The approach assumes that an effective offline policy is already available and might not perform well if the initial offline policy is suboptimal. Additionally, it may require tuning for different online environments.

Author Intelligence

Nathan S. de Lara

University of Toronto
nathan.delara@mail.utoronto.ca

Florian Shkurti

University of Toronto