Learning to Learn how to Learn: Self-Adaptive Visual Navigation using Meta-Learning (https://arxiv.org/abs/1812.00971)
-
Updated
Oct 22, 2019 - Python
#
ai2thor-environment
Here are 3 public repositories matching this topic...
Customisable Unified Physical Simulations (CUPS) for Reinforcement Learning. Experiments run on the ai2thor environment (http://ai2thor.allenai.org/) e.g. using A3C, RainbowDQN and A3C_GA (Gated Attention multi-modal fusion) for Task-Oriented Language Grounding (tasks specified by natural language instructions) e.g. "Pick up the Cup or else"
agent
wrapper
natural-language-processing
reinforcement-learning
robotics
simulation
cup
rainbow
pytorch
a3c
transfer-learning
cups
multi-task-learning
model-based
model-free
simulated-environments
natural-language-instructions
ai2thor-environment
openai-gym-interface
-
Updated
Mar 9, 2020 - Python
Evaluating pre-trained navigation agents under corruptions
computer-vision
deep-reinforcement-learning
visual-navigation
ai2thor-environment
embodied-ai
embodied-vision
-
Updated
Sep 7, 2021 - Python
Improve this page
Add a description, image, and links to the ai2thor-environment topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the ai2thor-environment topic, visit your repo's landing page and select "manage topics."