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Found 12 papers, 1 papers with code
Genie: Generative Interactive Environments
We introduce Genie, the first generative interactive environment trained in an unsupervised manner from unlabelled Internet videos.
Offline Actor-Critic Reinforcement Learning Scales to Large Models
We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning.
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities
Contemporary artificial intelligence systems exhibit rapidly growing abilities accompanied by the growth of required resources, expansive datasets and corresponding investments into computing infrastructure.
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.
A Generalist Dynamics Model for Control
We investigate the use of transformer sequence models as dynamics models (TDMs) for control.
Model Based Meta Learning of Critics for Policy Gradients
In this paper we present a framework to meta-learn the critic for gradient-based policy learning.
Learning Time-Invariant Reward Functions through Model-Based Inverse Reinforcement Learning
Inverse reinforcement learning is a paradigm motivated by the goal of learning general reward functions from demonstrated behaviours.
Multi-Modal Learning of Keypoint Predictive Models for Visual Object Manipulation
Our evaluation shows that our approach learns to consistently predict visual keypoints on objects in the manipulator's hand, and thus can easily facilitate learning an extended kinematic chain to include the object grasped in various configurations, from a few seconds of visual data.
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem.
Meta Learning via Learned Loss
We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.
Meta-Learning via Learned Loss
This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
Curious iLQR: Resolving Uncertainty in Model-based RL
In this work, we propose a model-based reinforcement learning (MBRL) framework that combines Bayesian modeling of the system dynamics with curious iLQR, an iterative LQR approach that considers model uncertainty.
Model-based Reinforcement Learning
reinforcement-learning
+1
