- Is DeepMind’s new reinforcement learning system a step toward general AI?
- DeepMind scientists: Reinforcement learning is enough for general AI
- What is reinforcement learning? How AI trains itself
- Discovering faster matrix multiplication algorithms with reinforcement learning
- Reinforcement Learning from scratch
Reinforcement learning is the subset of ML by which an algorithm can be programmed to respond to complex environments for optimal results.
This article was written by Steeve Huang. Reinforcement Learning (RL) refers to a kind of Machine Learning method in which the agent receives a delayed rewar…
DeepMind has released a new paper that shows impressive advances in reinforcement learning. How far does it bring us toward general AI?
«The key advantage of reinforcement learning is its ability to develop behavior by taking actions and getting feedback, similar to the way humans and animals learn by interacting with their environment»
In a new paper, scientists at DeepMind suggest that reward maximization and reinforcement learning are enough to develop artificial general intelligence.
«intelligence and its associated abilities will emerge not from formulating and solving complicated problems but by sticking to a simple but powerful principle: reward maximization.»
Posted by Karol Kurach, Research Lead and Olivier Bachem, Research Scientist, Google Research, Zürich The goal of reinforcement learning...
We’ve developed Random Network Distillation (RND), a prediction-based method for encouraging reinforcement learning agents to explore their environments through curiosity, which for the first time exceeds average human performance on Montezuma’s Revenge.
Google's releasing a reinforcement learning framework that makes it easier to train AI models with cutting-edge techniques.
A reinforcement learning approach based on AlphaZero is used to discover efficient and provably correct algorithms for matrix multiplication, finding faster algorithms for a variety of matrix sizes.
Inspired by a great tutorial at O’Reilly AI
By definition, learning from scratch is just about the least sample-efficient approach there can be.
June 24, 2018 note: If you want to cite an example from the post, please cite the paper which that example came from. If you want to cite the post as a whole, you can use the following BibTeX:
This is a deep dive into deep reinforcement learning. We will tackle a concrete problem with modern libraries such as TensorFlow, TensorBoard, Keras, and OpenAI Gym. You will learn how to implement…
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