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Found 14 papers, 5 papers with code
Modeling the Real World with High-Density Visual Particle Dynamics
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds containing 100K+ particles.
Learning rigid-body simulators over implicit shapes for large-scale scenes and vision
Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games.
Scaling Face Interaction Graph Networks to Real World Scenes
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design.
Learning 3D Particle-based Simulators from RGB-D Videos
Realistic simulation is critical for applications ranging from robotics to animation.
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning
We present a novel approach to address the challenge of generalization in offline reinforcement learning (RL), where the agent learns from a fixed dataset without any additional interaction with the environment.
Quantile Filtered Imitation Learning
We introduce quantile filtered imitation learning (QFIL), a novel policy improvement operator designed for offline reinforcement learning.
Offline RL Without Off-Policy Evaluation
In addition, we hypothesize that the strong performance of the one-step algorithm is due to a combination of favorable structure in the environment and behavior policy.
Decoupled Exploration and Exploitation Policies for Sample-Efficient Reinforcement Learning
This causes BBE to be actively detrimental to policy learning in many control tasks.
Evaluating representations by the complexity of learning low-loss predictors
We consider the problem of evaluating representations of data for use in solving a downstream task.
Offline Contextual Bandits with Overparameterized Models
We show that this discrepancy is due to the \emph{action-stability} of their objectives.
Disentangling Video with Independent Prediction
We propose an unsupervised variational model for disentangling video into independent factors, i. e. each factor's future can be predicted from its past without considering the others.
Model-Based Planning with Discrete and Continuous Actions
Action planning using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces.
Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning
There has been a recent explosion in the capabilities of game-playing artificial intelligence.
Understanding Visual Concepts with Continuation Learning
We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations.
