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Found 5 papers, 2 papers with code
Triangular Dropout: Variable Network Width without Retraining
After training, the layer can be arbitrarily reduced in width to exchange performance for narrowness.
Meta Arcade: A Configurable Environment Suite for Meta-Learning
Most approaches to deep reinforcement learning (DRL) attempt to solve a single task at a time.
The AI Arena: A Framework for Distributed Multi-Agent Reinforcement Learning
Advances in reinforcement learning (RL) have resulted in recent breakthroughs in the application of artificial intelligence (AI) across many different domains.
PICO: Primitive Imitation for COntrol
In this work, we explore a novel framework for control of complex systems called Primitive Imitation for Control PICO.
TanksWorld: A Multi-Agent Environment for AI Safety Research
In this work, we introduce the AI safety TanksWorld as an environment for AI safety research with three essential aspects: competing performance objectives, human-machine teaming, and multi-agent competition.
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