This list is big compilation of all things trying to adapt Reinforcement Learning techniques in real world.Whether it's mixing real world data into mix or trying to adapt simulations in a better way.It will also include some of Imitation Learning and Meta Learning along the way. If you have anything missing feel free to open a PR, I'm all for community contributions. I'm open to new categories so just read the contributing doc and provide a pull request. You can help also by starring our lovely repository and sharing 3 be safe! Any academic work done related to RL in real world.This is the other part of list, anything doesn't fit but still related gets here.
The course exposes oneself to the various real-life applications of Machine Learning and how ML is exploited in the various fields of life. So you know the theory of Machine Learning and know how to create your first algorithms. There are tons of courses out there about the underlying theory of Machine Learning which don't go any deeper – into the applications. This course is not one of them. Are you ready to apply all of the theory and knowledge to real life Machine Learning challenges?
We present counterfactual planning as a design approach for creating a range of safety mechanisms that can be applied in hypothetical future AI systems which have Artificial General Intelligence. The key step in counterfactual planning is to use an AGI machine learning system to construct a counterfactual world model, designed to be different from the real world the system is in. A counterfactual planning agent determines the action that best maximizes expected utility in this counterfactual planning world, and then performs the same action in the real world. We use counterfactual planning to construct an AGI agent emergency stop button, and a safety interlock that will automatically stop the agent before it undergoes an intelligence explosion. We also construct an agent with an input terminal that can be used by humans to iteratively improve the agent's reward function, where the incentive for the agent to manipulate this improvement process is suppressed. As an example of counterfactual planning in a non-agent AGI system, we construct a counterfactual oracle. As a design approach, counterfactual planning is built around the use of a graphical notation for defining mathematical counterfactuals. This two-diagram notation also provides a compact and readable language for reasoning about the complex types of self-referencing and indirect representation which are typically present inside machine learning agents.
Official World Record solve, an A.I. learns to solve the Rubik's Cube in under a SECOND! Made in MatLAB artificial-intelligence learns. Please share this video if you enjoyed, I will post a tutorial on how you can do this too, if this video surpasses a view(?) goal. Hello everyone, I had a lot of fun making this video and playing around with the artificial-intelligence. I've been a cuber since a very young age and I love to combine two things that I love a LOT.