We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task. This method performs as well as MAML, a broadly applicable meta-learning algorithm, while being simpler to implement and more computationally efficient.
Source: openai.com
Spoiler alert: Morse code doesn’t really need cracking. Its useful because messages can be sent using this code with minimal equipment, and I say it doesn’t need cracking because the code is well known and what the combinations of dots and dashes stand for is no secret. But, in theory, it is a substitution cipher — where each letter of the alphabet (and each digit) has some representation using dots and dashes, as illustrated below.
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Simulink provides a great training environment for DRL as it allows 3rd parties like Bonsai to integrate and control simulation models from the outside. This ability is one of the basic requirements for simulation platforms to be feasible for Deep Reinforcement Learning using Bonsai AI. More requirements can be found here.
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