Search Results for author:
Found 18 papers, 4 papers with code
LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models
Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance.
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities.
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay
In this work, we further exploit the information in the replay memory by treating it as an empirical \emph{Replay Memory MDP (RM-MDP)}.
A Simple Decentralized Cross-Entropy Method
To tackle this issue, we propose Decentralized CEM (DecentCEM), a simple but effective improvement over classical CEM, by using an ensemble of CEM instances running independently from one another, and each performing a local improvement of its own sampling distribution.
Dynamic Decision Frequency with Continuous Options
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals.
CWD: A Machine Learning based Approach to Detect Unknown Cloud Workloads
To address this problem of realizing the full potential of the underlying platform, we develop a machine learning based technique to characterize, profile and predict workloads running in the cloud environment.
Build generally reusable agent-environment interaction models
This paper tackles the problem of how to pre-train a model and make it generally reusable backbones for downstream task learning.
Auxiliary task discovery through generate-and-test
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning.
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy
Our focus on the role of the target policy of the auxiliary tasks is motivated by the fact that the target policy determines the behavior about which the agent wants to make a prediction and the state-action distribution that the agent is trained on, which further affects the main task learning.
Generalizable task representation learning from human demonstration videos: a geometric approach
We study the problem of generalizable task learning from human demonstration videos without extra training on the robot or pre-recorded robot motions.
Decentralized Cross-Entropy Method for Model-Based Reinforcement Learning
Further, we extend the decentralized approach to sequential decision-making problems where we show in 13 continuous control benchmark environments that it matches or outperforms the state-of-the-art CEM algorithms in most cases, under the same budget of the total number of samples for planning.
LISPR: An Options Framework for Policy Reuse with Reinforcement Learning
Our approach facilitates the learning of new policies by (1) maximizing the target MDP reward with the help of the black-box option, and (2) returning the agent to states in the learned initiation set of the black-box option where it is already optimal.
Offline Learning of Counterfactual Predictions for Real-World Robotic Reinforcement Learning
We consider real-world reinforcement learning (RL) of robotic manipulation tasks that involve both visuomotor skills and contact-rich skills.
Learning predictive representations in autonomous driving to improve deep reinforcement learning
Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits in performance and generalization are observed on unseen test roads in both simulation and on a real Jackal robot.
A Geometric Perspective on Visual Imitation Learning
We consider the problem of visual imitation learning without human supervision (e. g. kinesthetic teaching or teleoperation), nor access to an interactive reinforcement learning (RL) training environment.
Distributed estimation of principal support vector machines for sufficient dimension reduction
The principal support vector machines method (Li et al., 2011) is a powerful tool for sufficient dimension reduction that replaces original predictors with their low-dimensional linear combinations without loss of information.
Mapless Navigation among Dynamics with Social-safety-awareness: a reinforcement learning approach from 2D laser scans
We observe that our method demonstrates time-efficient path planning behavior with high success rate in mapless navigation tasks.
Robotics
Robot eye-hand coordination learning by watching human demonstrations: a task function approximation approach
Our proposed method can directly learn from raw videos, which removes the need for hand-engineered task specification.
Robotics
