research
∙
07/31/2023
Learning to Model the World with Language
To interact with humans in the world, agents need to understand the dive...
share
research
∙
05/23/2023
Video Prediction Models as Rewards for Reinforcement Learning
Specifying reward signals that allow agents to learn complex behaviors i...
research
∙
01/10/2023
Mastering Diverse Domains through World Models
General intelligence requires solving tasks across many domains. Current...
research
∙
10/24/2022
Evaluating Long-Term Memory in 3D Mazes
Intelligent agents need to remember salient information to reason in par...
research
∙
10/05/2022
Temporally Consistent Video Transformer for Long-Term Video Prediction
Generating long, temporally consistent video remains an open challenge i...
research
∙
06/28/2022
Masked World Models for Visual Control
Visual model-based reinforcement learning (RL) has the potential to enab...
research
∙
06/28/2022
DayDreamer: World Models for Physical Robot Learning
To solve tasks in complex environments, robots need to learn from experi...
research
∙
06/08/2022
Deep Hierarchical Planning from Pixels
Intelligent agents need to select long sequences of actions to solve com...
research
∙
12/07/2021
Information is Power: Intrinsic Control via Information Capture
Humans and animals explore their environment and acquire useful skills e...
research
∙
10/18/2021
Discovering and Achieving Goals via World Models
How can artificial agents learn to solve many diverse tasks in complex v...
research
∙
09/14/2021
Benchmarking the Spectrum of Agent Capabilities
Evaluating the general abilities of intelligent agents requires complex ...
research
∙
02/18/2021
Clockwork Variational Autoencoders
Deep learning has enabled algorithms to generate realistic images. Howev...
research
∙
12/21/2020
Evaluating Agents without Rewards
Reinforcement learning has enabled agents to solve challenging tasks in ...
research
∙
12/08/2020
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning
Model-based reinforcement learning (MBRL) methods have shown strong samp...
research
∙
11/27/2020
Skill Transfer via Partially Amortized Hierarchical Planning
To quickly solve new tasks in complex environments, intelligent agents n...
research
∙
10/05/2020
Mastering Atari with Discrete World Models
Intelligent agents need to generalize from past experience to achieve go...
research
∙
09/03/2020
Action and Perception as Divergence Minimization
We introduce a unified objective for action and perception of intelligen...
research
∙
06/07/2020
Sophisticated Inference
Active inference offers a first principle account of sentient behaviour,...
research
∙
05/12/2020
Planning to Explore via Self-Supervised World Models
Reinforcement learning allows solving complex tasks, however, the learni...
research
∙
12/03/2019
Dream to Control: Learning Behaviors by Latent Imagination
Learned world models summarize an agent's experience to facilitate learn...
research
∙
12/10/2018
Bayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation ...
research
∙
11/30/2018
Modulated Policy Hierarchies
Solving tasks with sparse rewards is a main challenge in reinforcement l...
research
∙
11/12/2018
Learning Latent Dynamics for Planning from Pixels
Planning has been very successful for control tasks with known environme...
research
∙
07/24/2018
Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
Obtaining reliable uncertainty estimates of neural network predictions i...
research
∙
07/04/2018
Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
Integrating model-free and model-based approaches in reinforcement learn...
research
∙
04/27/2018
Sim-to-Real: Learning Agile Locomotion For Quadruped Robots
Designing agile locomotion for quadruped robots often requires extensive...
research
∙
11/28/2017
Generative Interest Estimation for Document Recommendations
Learning distributed representations of documents has pushed the state-o...
research
∙
09/08/2017
TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow
We introduce TensorFlow Agents, an efficient infrastructure paradigm for...
research
∙
06/18/2017
Learning Hierarchical Information Flow with Recurrent Neural Modules
We propose ThalNet, a deep learning model inspired by neocortical commun...
research
∙
10/07/2016