Search Results for author:
Found 27 papers, 7 papers with code
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
Learning Multi-Objective Curricula for Deep Reinforcement Learning
Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).
Context-Specific Representation Abstraction for Deep Option Learning
Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.
Egocentric Activity Recognition and Localization on a 3D Map
Our model takes the inputs of a Hierarchical Volumetric Representation (HVR) of the environment and an egocentric video, infers the 3D action location as a latent variable, and recognizes the action based on the video and contextual cues surrounding its potential locations.
4D Human Body Capture from Egocentric Video via 3D Scene Grounding
We introduce a novel task of reconstructing a time series of second-person 3D human body meshes from monocular egocentric videos.
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games
This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm.
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning
A fundamental challenge in multiagent reinforcement learning is to learn beneficial behaviors in a shared environment with other simultaneously learning agents.
Deep RL With Information Constrained Policies: Generalization in Continuous Control
We focus on the model-free reinforcement learning (RL) setting and formalize our approach in terms of an information-theoretic constraint on the complexity of learned policies.
In the Eye of the Beholder: Gaze and Actions in First Person Video
Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV.
Coagent Networks Revisited
This work is aiming to discuss and close some of the gaps in the literature on models using options (and more generally coagents).
On the Role of Weight Sharing During Deep Option Learning
In this work we note that while this key assumption of the policy gradient theorems of option-critic holds in the tabular case, it is always violated in practice for the deep function approximation setting.
Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video
Motivated by this, we adopt intentional hand movement as a future representation and propose a novel deep network that jointly models and predicts the egocentric hand motion, interaction hotspots and future action.
CAPACITY-LIMITED REINFORCEMENT LEARNING: APPLICATIONS IN DEEP ACTOR-CRITIC METHODS FOR CONTINUOUS CONTROL
We formalize this type of bounded rationality in terms of an information-theoretic constraint on the complexity of policies that agents seek to learn.
Attention Distillation for Learning Video Representations
To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition.
Ranked #33 on
Action Recognition
on UCF101
Learning Hierarchical Teaching Policies for Cooperative Agents
Collective learning can be greatly enhanced when agents effectively exchange knowledge with their peers.
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference
In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples.
Learning Abstract Options
Building systems that autonomously create temporal abstractions from data is a key challenge in scaling learning and planning in reinforcement learning.
In the Eye of Beholder: Joint Learning of Gaze and Actions in First Person Video
We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera.
Learning to Teach in Cooperative Multiagent Reinforcement Learning
The problem of teaching to improve agent learning has been investigated by prior works, but these approaches make assumptions that prevent application of teaching to general multiagent problems, or require domain expertise for problems they can apply to.
Faster Reinforcement Learning with Expert State Sequences
In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are not available.
The Eigenoption-Critic Framework
Eigenoptions (EOs) have been recently introduced as a promising idea for generating a diverse set of options through the graph Laplacian, having been shown to allow efficient exploration.
Eigenoption Discovery through the Deep Successor Representation
Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning.
Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
We implement two variants of multi-robot Search and Rescue (SAR) domains (with and without obstacles) on hardware to demonstrate the learned policies can effectively control a team of distributed robots to cooperate in a partially observable stochastic environment.
Socially Aware Motion Planning with Deep Reinforcement Learning
For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e. g., passing on the right).
Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning
Finding feasible, collision-free paths for multiagent systems can be challenging, particularly in non-communicating scenarios where each agent's intent (e. g. goal) is unobservable to the others.
Multiagent Systems
Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs).
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters.
