TalkRL: The Reinforcement Learning Podcast cover image

TalkRL: The Reinforcement Learning Podcast

Ian Osband

Mar 7, 2024

A Research scientist at OpenAI discusses information theory and RL, joint predictions, and Epistemic Neural Networks. They explore challenges in reinforcement learning, handling uncertainty, and balancing exploration vs exploitation. The podcast delves into the importance of joint predictive distributions, Thompson sampling approximation, and uncertainty frameworks in Large Language Models (LLMs).

01:08:26

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Quick takeaways

Reinforcement learning requires navigating exploration challenges inherent in noisy environments for optimal decision-making under uncertainty.

Information theory provides a framework for balancing exploration and exploitation through concepts like mutual information and joint predictions in reinforcement learning algorithms.

Epistemic neural networks offer a nuanced approach to predictive modeling, emphasizing joint predictions to enhance decision-making efficiency in complex scenarios.

Deep dives

Understanding Reinforcement Learning and Decision-Making Under Uncertainty

Reinforcement learning involves solving problems associated with exploration, long-term consequences, and generalization compared to supervised learning. Dealing with uncertainty is crucial, including epistemic uncertainty, which distinguishes between chance and knowledge uncertainty. Researchers, like Dr. Ian Osmand, focus on learning agents that make optimal decisions under uncertain conditions, contributing key insights to the field.

Balancing statistical and computational complexity in decision-making

Focus on Problems, Not Solution Methods

Distinguishing between Marginal and Joint Predictions

Efficiency in Training Models with Bayesian Inference

1.

Introduction

2min

2.

Exploring Reinforcement Learning Concepts and Uncertainty

7min

3.

Exploring the Role of Information Theory in Reinforcement Learning

6min

4.

Balancing Exploration and Exploitation in Reinforcement Learning

8min

5.

Understanding Joint Predictions in Machine Learning

9min

6.

Exploring Joint Prediction and Uncertainty in Neural Networks

23min

7.

Exploring Thompson Sampling Approximation in Reinforcement Learning

5min

8.

Exploring Uncertainty Frameworks in LLMs

9min

Ian Osband is a Research scientist at OpenAI (ex DeepMind, Stanford) working on decision making under uncertainty.

We spoke about:

- Information theory and RL

- Exploration, epistemic uncertainty and joint predictions

- Epistemic Neural Networks and scaling to LLMs

Featured References

Reinforcement Learning, Bit by Bit
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen

From Predictions to Decisions: The Importance of Joint Predictive Distributions

Zheng Wen, Ian Osband, Chao Qin, Xiuyuan Lu, Morteza Ibrahimi, Vikranth Dwaracherla, Mohammad Asghari, Benjamin Van Roy

Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Approximate Thompson Sampling via Epistemic Neural Networks

Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy

Additional References

Thesis defence, Ian Osband
Homepage, Ian Osband
Epistemic Neural Networks at Stanford RL Forum
Behaviour Suite for Reinforcement Learning, Osband et al 2019
Efficient Exploration for LLMs, Dwaracherla et al 2024

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