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.
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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 outcomes of sessions.
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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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GoodAI has publically released the Badger Architecture which is the culmination of five years of work at GoodAI. Find out more about it and read the paper.
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