Last week GoodAI organized the first Meta-Learning & Multi-Agent Learning Workshop which throughout the week saw over 60 participants from across the world take part including speakers from Google Brain, DeepMind, OpenAI, University of Oxford, Stanford University, MIT and many.
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Usually, in Deep Learning, the tasks are solved by a big monolithic Artificial Neural Network.
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There seem to be at least two views on the topic of multi-agentness/collectivity in badger, depending on whether we are talking about the potential of badger vs. the technical details of how learning in badger occurs, i.e. the why and.
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To understand why Badger is hard to train, we need to understand first how Badger learns, using a toy task. We try to understand the plateaus, what happens during this period, and why the plateaus are there in the first.
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Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are often uncomputable, or lack practical implementations.
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We recently organized an internal workshop with a number of external collaborators to advance the progress of various challenging topics related to the Badger architecture. In this post, we would like to share the posed questions and the outcomes of the nine.
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Currently, we are experimenting with an experimental setup proposed in our Badger paper. One of the areas of explorations is an evaluation of suitability of various training settings: supervised learning, Deep Reinforcement Learning (RL), and evolutionary optimization.
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This guide shows how to use Pytorch’s C++ API to use neural networks in Unity. We can use this with existing Python-based models, by freezing the execution trace into a binary file that is loaded by the library at runtime.
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At GoodAI, we’re interested in multi-agent architectures that can learn to rapidly adapt to new and unseen environments we expect the behavior and adaptation to be learned through communication of homogeneous units inside a single agent, allowing for better generalization.
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The idea of the Badger architecture is to make a learning agent with increased generality by virtue of allowing task-specific learning to occur in the activations of an extensible pool of ‘experts’ who all share the same weights.
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