Search Results for author:
Found 13 papers, 4 papers with code
Scaling Instructable Agents Across Many Simulated Worlds
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI.
Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language.
Imitating Interactive Intelligence
These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone.
Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text
Here, we propose a conceptually simple method for training instruction-following agents with deep RL that are robust to natural human instructions.
Multiplicative Interactions and Where to Find Them
We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text
We address this issue by integrating language encoders that are pretrained on large text corpora into a situated, instruction-following agent.
A Generalized Framework for Population Based Training
Population Based Training (PBT) is a recent approach that jointly optimizes neural network weights and hyperparameters which periodically copies weights of the best performers and mutates hyperparameters during training.
Dynamic Control Flow in Large-Scale Machine Learning
We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system.
Unsupervised Predictive Memory in a Goal-Directed Agent
Animals execute goal-directed behaviours despite the limited range and scope of their sensors.
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.
Ranked #3 on
Atari Games
on Atari 2600 Skiing
(using extra training data)
The Predictron: End-To-End Learning and Planning
One of the key challenges of artificial intelligence is to learn models that are effective in the context of planning.
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
SAM learns with comparable data efficiency to existing models on a range of synthetic tasks and one-shot Omniglot character recognition, and can scale to tasks requiring $100,\! 000$s of time steps and memories.
Ranked #6 on
Question Answering
on bAbi
(Mean Error Rate metric)
Asynchronous Methods for Deep Reinforcement Learning
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.
Ranked #9 on
Atari Games
on Atari 2600 Star Gunner
