Harri Valpola: System 2 AI and Planning in Model-Based Reinforcement Learning

In this episode of Machine Learning Street Talk, Tim Scarfe, Yannic Kilcher and Connor Shorten interviewed Harri Valpola, CEO and Founder of Curious AI. We continued our discussion of System 1 and System 2 thinking in Deep Learning, as well as miscellaneous topics around Model-based Reinforcement Learning. Dr. Valpola describes some of the challenges of modelling industrial control processes such as water sewage filters and paper mills with the use of model-based RL. Dr. Valpola and his collaborators recently published “Regularizing Trajectory Optimization with Denoising Autoencoders” that addresses some of the concerns of planning algorithms that exploit inaccuracies in their world models!

00:00:00 Intro to Harri and Curious AI System1/System 2

00:04:50 Background on model-based RL challenges from Tim

00:06:26 Other interesting research papers on model-based RL from Connor

00:08:36 Intro to Curious AI recent NeurIPS paper on model-based RL and denoising autoencoders from Yannic

00:21:00 Main show kick off, system 1/2

00:31:50 Where does the simulator come from?

00:33:59 Evolutionary priors

00:37:17 Consciousness

00:40:37 How does one build a company like Curious AI?

00:46:42 Deep Q Networks

00:49:04 Planning and Model based RL

00:53:04 Learning good representations

00:55:55 Typical problem Curious AI might solve in industry

01:00:56 Exploration

01:08:00 Their paper - regularizing trajectory optimization with denoising

01:13:47 What is Epistemic uncertainty

01:16:44 How would Curious develop these models

01:18:00 Explainability and simulations

01:22:33 How system 2 works in humans

01:26:11 Planning

01:27:04 Advice for starting an AI company

01:31:31 Real world implementation of planning models

01:33:49 Publishing research and openness

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Regularizing Trajectory Optimization with Denoising Autoencoders: https://papers.nips.cc/paper/8552-regularizing-trajectory-optimization-with-denoising-autoencoders.pdf

Pulp, Paper & Packaging: A Future Transformed through Deep Learning: https://thecuriousaicompany.com/pulp-paper-packaging-a-future-transformed-through-deep-learning/

Curious AI: https://thecuriousaicompany.com/

Harri Valpola Publications: https://scholar.google.com/citations?user=1uT7-84AAAAJ&hl=en&oi=ao

Some interesting papers around Model-Based RL:

GameGAN: https://cdn.arstechnica.net/wp-content/uploads/2020/05/Nvidia_GameGAN_Research.pdf

Plan2Explore: https://ramanans1.github.io/plan2explore/

World Models: https://worldmodels.github.io/

MuZero: https://arxiv.org/pdf/1911.08265.pdf

PlaNet: A Deep Planning Network for RL: https://ai.googleblog.com/2019/02/introducing-planet-deep-planning.html

Dreamer: Scalable RL using World Models: https://ai.googleblog.com/2020/03/introducing-dreamer-scalable.html

Model Based RL for Atari: https://arxiv.org/pdf/1903.00374.pdf