Search Results for author:
Found 23 papers, 7 papers with code
Semi-Supervised One-Shot Imitation Learning
We utilize this embedding space and the clustering it supports to self-generate pairings between trajectories in the large unpaired dataset.
HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of Effective Mechanical Properties using Neural Operators
Homogenization is an essential tool for studying multiscale physical phenomena.
Teaching Large Language Models to Reason with Reinforcement Learning
Surprisingly, we find the sample complexity of Expert Iteration is similar to that of PPO, requiring at most on the order of $10^6$ samples to converge from a pretrained checkpoint.
Learning to Model the World with Language
While current agents can learn to execute simple language instructions, we aim to build agents that leverage diverse language -- language like "this button turns on the TV" or "I put the bowls away" -- that conveys general knowledge, describes the state of the world, provides interactive feedback, and more.
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradient to maximize the feedback-trained reward.
Vision-Language Models as Success Detectors
Detecting successful behaviour is crucial for training intelligent agents.
Aligning Text-to-Image Models using Human Feedback
Our results demonstrate the potential for learning from human feedback to significantly improve text-to-image models.
Guiding Pretraining in Reinforcement Learning with Large Language Models
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function.
It Takes Four to Tango: Multiagent Selfplay for Automatic Curriculum Generation
Training such agents efficiently requires automatic generation of a goal curriculum.
Bayesian Imitation Learning for End-to-End Mobile Manipulation
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a mobile manipulator.
Practical Imitation Learning in the Real World via Task Consistency Loss
To the best of our knowledge, this is the first work to tackle latched door opening from a purely end-to-end learning approach, where the task of navigation and manipulation are jointly modeled by a single neural network.
It Takes Four to Tango: Multiagent Self Play for Automatic Curriculum Generation
We are interested in training general-purpose reinforcement learning agents that can solve a wide variety of goals.
Auto-Tuned Sim-to-Real Transfer
Policies trained in simulation often fail when transferred to the real world due to the `reality gap' where the simulator is unable to accurately capture the dynamics and visual properties of the real world.
Wirelessly Powered Federated Edge Learning: Optimal Tradeoffs Between Convergence and Power Transfer
To derive guidelines on deploying the resultant wirelessly powered FEEL (WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point).
Robust Reinforcement Learning using Adversarial Populations
Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed.
Out-of-Distribution Generalization
reinforcement-learning
+2
Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computing
Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation.
Information Theory Signal Processing Information Theory
AvE: Assistance via Empowerment
One difficulty in using artificial agents for human-assistive applications lies in the challenge of accurately assisting with a person's goal(s).
One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysis
We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme.
Information Theory Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Signal Processing Information Theory
An Introduction to Communication Efficient Edge Machine Learning
The main theme in the area is to design new communication techniques and protocols for efficient implementation of different distributed learning frameworks (i. e., federated learning) in wireless networks.
Information Theory Signal Processing Information Theory
High-Dimensional Stochastic Gradient Quantization for Communication-Efficient Edge Learning
First, the framework features a practical hierarchical architecture for decomposing the stochastic gradient into its norm and normalized block gradients, and efficiently quantizes them using a uniform quantizer and a low-dimensional codebook on a Grassmann manifold, respectively.
Energy-Efficient Radio Resource Allocation for Federated Edge Learning
To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling.
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning.
