reinforcement learning – Page 4 – Andrew Fairless, Ph.D.

What I Read: Permutation-Invariant Neural Networks for Reinforcement Learning

By Andrew Fairless on October 5, 2021September 21, 2021

https://arxiv.org/abs/2109.02869v1 The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement LearningYujin Tang, David Ha “…we build systems that feed each sensory input from the environment into distinct, butContinue readingWhat I Read: Permutation-Invariant Neural Networks for Reinforcement Learning

What I Read: How Generally Capable Agents Trained

By Andrew Fairless on September 23, 2021September 4, 2021

https://www.lesswrong.com/posts/DreKBuMvK7fdESmSJ/how-deepmind-s-generally-capable-agents-were-trained How DeepMind’s Generally Capable Agents Were Trainedby 1a3orn20th Aug 2021 “One of DeepMind’s latest papers… explains how DeepMind produced agents that can successfully play games as complex as hide-and-seekContinue readingWhat I Read: How Generally Capable Agents Trained

What I Read: AI-Generating Algorithms, Evolutionary RL

By Andrew Fairless on August 19, 2021July 22, 2021

https://youtu.be/FJJpmxO2Ci8 GECCO2021 – wkspk105 – WS – EvoRL – AI-Generating Algorithms: A Unique Opportunity for the […]AI-Generating Algorithms: A Unique Opportunity for the Evolutionary RL Community (wkspk105, WS – EvoRL)JunContinue readingWhat I Read: AI-Generating Algorithms, Evolutionary RL

What I Read: Model Free

By Andrew Fairless on July 26, 2021July 12, 2021

http://www.argmin.net/2018/04/19/pid/ The Best Things in Life Are Model FreeBen RechtApr 19, 2018 “The odd thing about statistics and ML research these days is that everyone knows about gradient descent, butContinue readingWhat I Read: Model Free

What I Read: Five types of thinking

By Andrew Fairless on July 7, 2021June 22, 2021

https://towardsdatascience.com/five-types-of-thinking-for-a-high-performing-data-scientist-8ab70d70c23b?gi=772956cb941c Thinking about thinking (Part 1)Five types of thinking for a high performing data scientistFrom mental models to computational thinkingAnand S RaoApr 25 “As data scientists, we have to understandContinue readingWhat I Read: Five types of thinking

What I Read: RL, Decentralized Multi-agent Navigation

By Andrew Fairless on May 25, 2021May 1, 2021

https://ai.googleblog.com/2021/04/model-based-rl-for-decentralized-multi.html Model-Based RL for Decentralized Multi-agent NavigationWednesday, April 28, 2021Posted by Rose E. Wang, Student Researcher and Aleksandra Faust, Staff Research Scientist, Google Research “… in many real-world applications aligningContinue readingWhat I Read: RL, Decentralized Multi-agent Navigation

What I Read: Continual Learning, Amnesia, Neural Networks

By Andrew Fairless on March 22, 2021March 7, 2021

https://medium.com/dataseries/ibm-uses-continual-learning-to-avoid-the-amnesia-problem-in-neural-networks-ae8241e1f3a3 IBM Uses Continual Learning to Avoid The Amnesia Problem in Neural NetworksUsing continual learning might avoid the famous catastrophic forgetting problem in neural networks.Jesus RodriguezJan 25 “Building neural networksContinue readingWhat I Read: Continual Learning, Amnesia, Neural Networks

What I Read: Neural Text Generation

By Andrew Fairless on March 11, 2021March 7, 2021

https://lilianweng.github.io/lil-log/2021/01/02/controllable-neural-text-generation.html Controllable Neural Text GenerationJan 2, 2021Lilian Weng “How to steer a powerful unconditioned language model? In this post, we will delve into several approaches for controlled content generation withContinue readingWhat I Read: Neural Text Generation

What I Read: Interpretability in Machine Learning

By Andrew Fairless on February 20, 2021January 21, 2021

https://thegradient.pub/interpretability-in-ml-a-broad-overview/ Interpretability in Machine Learning: An Overview21.Nov.2020Owen Shen “Broadly, interpretability focuses on the how. It’s focused on getting some notion of an explanation for the decisions made by our models….Continue readingWhat I Read: Interpretability in Machine Learning

What I Read: Revisiting Sutton’s Bitter Lesson for AI

By Andrew Fairless on February 15, 2021January 18, 2021

https://blog.exxactcorp.com/compute-goes-brrr-revisiting-suttons-bitter-lesson-artificial-intelligence/ Deep LearningCompute Goes Brrr: Revisiting Sutton’s Bitter Lesson for Artificial IntelligenceMarketing, October 27, 2020 “The main driver of AI progress, according to Sutton, is the increasing availability of computeContinue readingWhat I Read: Revisiting Sutton’s Bitter Lesson for AI

Tag: reinforcement learning