Search Results for author: Miao Liu

Found 27 papers, 7 papers with code

Learning Multi-Objective Curricula for Deep Reinforcement Learning

no code implementations6 Oct 2021 Jikun Kang, Miao Liu, Abhinav Gupta, Chris Pal, Xue Liu, Jie Fu

Various automatic curriculum learning (ACL) methods have been proposed to improve the sample efficiency and final performance of deep reinforcement learning (DRL).

Curriculum Learning

Context-Specific Representation Abstraction for Deep Option Learning

1 code implementation20 Sep 2021 Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How

Hierarchical reinforcement learning has focused on discovering temporally extended actions, such as options, that can provide benefits in problems requiring extensive exploration.

Hierarchical Reinforcement Learning

Egocentric Activity Recognition and Localization on a 3D Map

no code implementations20 May 2021 Miao Liu, Lingni Ma, Kiran Somasundaram, Yin Li, Kristen Grauman, James M. Rehg, Chao Li

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.

Action Localization Action Recognition +2

4D Human Body Capture from Egocentric Video via 3D Scene Grounding

no code implementations26 Nov 2020 Miao Liu, Dexin Yang, Yan Zhang, Zhaopeng Cui, James M. Rehg, Siyu Tang

We introduce a novel task of reconstructing a time series of second-person 3D human body meshes from monocular egocentric videos.

Time Series

Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games

no code implementations23 Nov 2020 Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims

This paper introduces an information-theoretic constraint on learned policy complexity in the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) reinforcement learning algorithm.

Continual Learning Continuous Control

A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning

1 code implementation31 Oct 2020 Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How

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

no code implementations9 Oct 2020 Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro

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.

Continuous Control

In the Eye of the Beholder: Gaze and Actions in First Person Video

no code implementations31 May 2020 Yin Li, Miao Liu, James M. Rehg

Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV.

Action Recognition Gaze Estimation

Coagent Networks Revisited

1 code implementation28 Jan 2020 Modjtaba Shokrian Zini, Mohammad Pedramfar, Matthew Riemer, Miao Liu

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

no code implementations31 Dec 2019 Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro

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.

Atari Games

Forecasting Human-Object Interaction: Joint Prediction of Motor Attention and Actions in First Person Video

1 code implementation ECCV 2020 Miao Liu, Siyu Tang, Yin Li, James Rehg

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.

Action Anticipation Human-Object Interaction Detection

CAPACITY-LIMITED REINFORCEMENT LEARNING: APPLICATIONS IN DEEP ACTOR-CRITIC METHODS FOR CONTINUOUS CONTROL

no code implementations25 Sep 2019 Tyler James Malloy, Matthew Riemer, Miao Liu, Tim Klinger, Gerald Tesauro, Chris R. Sims

We formalize this type of bounded rationality in terms of an information-theoretic constraint on the complexity of policies that agents seek to learn.

Continuous Control

Attention Distillation for Learning Video Representations

no code implementations5 Apr 2019 Miao Liu, Xin Chen, Yun Zhang, Yin Li, James M. Rehg

To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition.

Action Recognition Video Recognition

Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference

1 code implementation ICLR 2019 Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro

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.

Continual Learning Meta-Learning

Learning Abstract Options

no code implementations NeurIPS 2018 Matthew Riemer, Miao Liu, Gerald Tesauro

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

no code implementations ECCV 2018 Yin Li, Miao Liu, James M. Rehg

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.

Action Recognition Gaze Estimation

Learning to Teach in Cooperative Multiagent Reinforcement Learning

no code implementations20 May 2018 Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How

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

no code implementations ICLR 2018 Xiaoxiao Guo, Shiyu Chang, Mo Yu, Miao Liu, Gerald Tesauro

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.

Imitation Learning

The Eigenoption-Critic Framework

no code implementations11 Dec 2017 Miao Liu, Marlos C. Machado, Gerald Tesauro, Murray Campbell

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.

Efficient Exploration Hierarchical Reinforcement Learning

Eigenoption Discovery through the Deep Successor Representation

no code implementations ICLR 2018 Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell

Options in reinforcement learning allow agents to hierarchically decompose a task into subtasks, having the potential to speed up learning and planning.

Atari Games Representation Learning

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions

no code implementations24 Jul 2017 Miao Liu, Kavinayan Sivakumar, Shayegan Omidshafiei, Christopher Amato, Jonathan P. How

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.

Decision Making Decision Making Under Uncertainty

Socially Aware Motion Planning with Deep Reinforcement Learning

2 code implementations26 Mar 2017 Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How

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).

Autonomous Navigation Motion Planning

Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning

no code implementations26 Sep 2016 Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How

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

no code implementations1 May 2015 Miao Liu, Christopher Amato, Xuejun Liao, Lawrence Carin, Jonathan P. How

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

1 code implementation NeurIPS 2013 Trevor Campbell, Miao Liu, Brian Kulis, Jonathan P. How, Lawrence Carin

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.

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