Search Results for author:
Found 7 papers, 3 papers with code
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning
In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Autonomous Curiosity for Real-Time Training Onboard Robotic Agents
Learning requires both study and curiosity.
Learning to Learn in Simulation
We use a 3D simulation environment and deep reinforcement learning to train a curiosity agent to, in turn, train the object detection model.
Obstacle Tower: A Generalization Challenge in Vision, Control, and Planning
Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment.
Unity: A General Platform for Intelligent Agents
Recent advances in artificial intelligence have been driven by the presence of increasingly realistic and complex simulated environments.
ClickBAIT-v2: Training an Object Detector in Real-Time
Modern deep convolutional neural networks (CNNs) for image classification and object detection are often trained offline on large static datasets.
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks
Today's general-purpose deep convolutional neural networks (CNN) for image classification and object detection are trained offline on large static datasets.
