RIML Lab

🧠 RL Journal Club: This Week's Session

🤝 We invite you to join us for this week's RL Journal Club session, where we will dive into a minimalist approach to offline reinforcement learning. In this session, we will explore how simplifying algorithms can lead to more robust and efficient models in RL, challenging the necessity of complex modifications commonly seen in recent advancements.

✅ This Week's Presentation:

🔹 Title: Revisiting the Minimalist Approach to Offline Reinforcement Learning
🔸 Presenter: Professor Mohammad Hossein Rohban
🌀 Abstract: This presentation will delve into the trade-offs between simplicity and performance in offline RL algorithms. We will review the minimalist approach proposed in the paper, which re-evaluates core algorithmic features and shows that simpler models can achieve performance on par with more intricate methods. The discussion will include experimental results that demonstrate how stripping away complexity can lead to more effective learning, providing fresh insights into the design of RL systems.

The presentation will be based on the following paper:

▪️ Revisiting the Minimalist Approach to Offline Reinforcement Learning (https://arxiv.org/abs/2305.09836)

Session Details:

📅 Date: Tuesday
🕒 Time: 4:00 - 5:00 PM
🌐 Location: Online at https://vc.sharif.edu/ch/rohban
📍 For in-person attendance, please message me on Telegram at @alirezanobakht78

☝️ Note: The discussion is open to everyone, but we can only host students of Sharif University of Technology in person.

💯 Join us for an insightful session where we rethink how much complexity is truly necessary for effective offline reinforcement learning! Don't miss this chance to deepen your understanding of RL methodologies.

✌️ We look forward to your participation!
#RLJClub #JClub #RIML #SUT #AI #RL

arXiv.org

Revisiting the Minimalist Approach to Offline Reinforcement Learning

Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity. While these...

www.tgoop.com/RIMLLab/135

1.9K viewsمحراب مرادزاده, Sep 16 at 13:52

🧠 RL Journal Club: This Week's Session

🤝 We invite you to join us for this week's RL Journal Club session, where we will dive into a minimalist approach to offline reinforcement learning. In this session, we will explore how simplifying algorithms can lead to more robust and efficient models in RL, challenging the necessity of complex modifications commonly seen in recent advancements.

✅ This Week's Presentation:

🔹 Title: Revisiting the Minimalist Approach to Offline Reinforcement Learning
🔸 Presenter: Professor Mohammad Hossein Rohban
🌀 Abstract: This presentation will delve into the trade-offs between simplicity and performance in offline RL algorithms. We will review the minimalist approach proposed in the paper, which re-evaluates core algorithmic features and shows that simpler models can achieve performance on par with more intricate methods. The discussion will include experimental results that demonstrate how stripping away complexity can lead to more effective learning, providing fresh insights into the design of RL systems.

The presentation will be based on the following paper:

▪️ Revisiting the Minimalist Approach to Offline Reinforcement Learning (https://arxiv.org/abs/2305.09836)

Session Details:

📅 Date: Tuesday
🕒 Time: 4:00 - 5:00 PM
🌐 Location: Online at https://vc.sharif.edu/ch/rohban
📍 For in-person attendance, please message me on Telegram at @alirezanobakht78

☝️ Note: The discussion is open to everyone, but we can only host students of Sharif University of Technology in person.

💯 Join us for an insightful session where we rethink how much complexity is truly necessary for effective offline reinforcement learning! Don't miss this chance to deepen your understanding of RL methodologies.

✌️ We look forward to your participation!
#RLJClub #JClub #RIML #SUT #AI #RL

BY RIML Lab