Search Results for author:
Found 10 papers, 7 papers with code
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents
We introduce AMAGO, an in-context Reinforcement Learning (RL) agent that uses sequence models to tackle the challenges of generalization, long-term memory, and meta-learning.
PGrad: Learning Principal Gradients For Domain Generalization
In this work, we develop a novel DG training strategy, we call PGrad, to learn a robust gradient direction, improving models' generalization ability on unseen domains.
Launchpad: Learning to Schedule Using Offline and Online RL Methods
We utilize Offline RL as a launchpad to learn effective scheduling policies from prior experience collected using Oracle or heuristic policies.
RARE: Renewable Energy Aware Resource Management in Datacenters
Finally, we demonstrate that the DRL scheduler can learn from and improve upon existing heuristic policies using Offline Learning.
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets
A thorough investigation on a custom benchmark helps identify several key challenges involved in learning from high-noise datasets.
ST-MAML: A Stochastic-Task based Method for Task-Heterogeneous Meta-Learning
This paper proposes a novel method, ST-MAML, that empowers model-agnostic meta-learning (MAML) to learn from multiple task distributions.
Long-Range Transformers for Dynamic Spatiotemporal Forecasting
Multivariate time series forecasting focuses on predicting future values based on historical context.
Towards Automatic Actor-Critic Solutions to Continuous Control
Model-free off-policy actor-critic methods are an efficient solution to complex continuous control tasks.
Measuring Visual Generalization in Continuous Control from Pixels
Self-supervised learning and data augmentation have significantly reduced the performance gap between state and image-based reinforcement learning agents in continuous control tasks.
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
TextAttack also includes data augmentation and adversarial training modules for using components of adversarial attacks to improve model accuracy and robustness.
