AI agents often operate in partially observable environments, where only part of the environment state is visible at any given time. An agent in such an environment needs memory to compute effective actions from the history of its actions.
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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 and MIT.
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One of the properties of the Badger architecture is modularity: instead of using one big neural network, the Badger should be composed of many small Experts which solve the whole task in a collaborative manner.
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GoodAI recently hosted a virtual workshop with a number of external collaborators in order to address some of the crucial open questions related to our Badger Architecture.
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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.
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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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