Roy Fox | DeepAI

DeepAI AI Chat

Featured Co-authors

Pieter Abbeel
254 publications
Dawn Song
120 publications
Ken Goldberg
104 publications
Tuomas Sandholm
77 publications
Ion Stoica
73 publications
Sameer Singh
72 publications
Marc Lanctot
44 publications
Pierre Baldi
43 publications
Sylvain Calinon
37 publications
Brijen Thananjeyan
26 publications
Ioannis Panageas
25 publications

∙ 09/16/2022

Reducing Variance in Temporal-Difference Value Estimation via Ensemble of Deep Networks

In temporal-difference reinforcement learning algorithms, variance in va...

0 Litian Liang, et al. ∙

∙ 07/19/2022

Feasible Adversarial Robust Reinforcement Learning for Underspecified Environments

Robust reinforcement learning (RL) considers the problem of learning pol...

1 JB Lanier, et al. ∙

∙ 07/13/2022

Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games

In competitive two-agent environments, deep reinforcement learning (RL) ...

5 Stephen McAleer, et al. ∙

∙ 05/25/2022

Learning to Query Internet Text for Informing Reinforcement Learning Agents

Generalization to out of distribution tasks in reinforcement learning is...

0 Kolby Nottingham, et al. ∙

∙ 01/19/2022

Anytime PSRO for Two-Player Zero-Sum Games

Policy space response oracles (PSRO) is a multi-agent reinforcement lear...

1 Stephen McAleer, et al. ∙

∙ 12/06/2021

Target Entropy Annealing for Discrete Soft Actor-Critic

Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in ...

2 Yaosheng Xu, et al. ∙

∙ 11/28/2021

Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning

Maximum Entropy Reinforcement Learning (MaxEnt RL) algorithms such as So...

0 Dailin Hu, et al. ∙

∙ 10/28/2021

Temporal-Difference Value Estimation via Uncertainty-Guided Soft Updates

Temporal-Difference (TD) learning methods, such as Q-Learning, have prov...

4 Litian Liang, et al. ∙

∙ 10/20/2021

Independent Natural Policy Gradient Always Converges in Markov Potential Games

Multi-agent reinforcement learning has been successfully applied to full...

5 Roy Fox, et al. ∙

∙ 09/05/2021

Modular Framework for Visuomotor Language Grounding

Natural language instruction following tasks serve as a valuable test-be...

14 Kolby Nottingham, et al. ∙

∙ 06/07/2021

Improving Social Welfare While Preserving Autonomy via a Pareto Mediator

Machine learning algorithms often make decisions on behalf of agents wit...

4 Stephen McAleer, et al. ∙

∙ 03/11/2021

XDO: A Double Oracle Algorithm for Extensive-Form Games

Policy Space Response Oracles (PSRO) is a deep reinforcement learning al...

4 Stephen McAleer, et al. ∙

∙ 02/08/2021

A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks

A* search is an informed search algorithm that uses a heuristic function...

8 Forest Agostinelli, et al. ∙

∙ 06/15/2020

Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games

Finding approximate Nash equilibria in zero-sum imperfect-information ga...

0 Stephen McAleer, et al. ∙

∙ 12/29/2019

Hierarchical Variational Imitation Learning of Control Programs

Autonomous agents can learn by imitating teacher demonstrations of the i...

17 Roy Fox, et al. ∙

∙ 11/19/2018

Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models

Generalizing manipulation skills to new situations requires extracting i...

0 Ajay Kumar Tanwani, et al. ∙

∙ 01/31/2018

Model-Free Error Detection and Recovery for Robot Learning from Demonstration

Learning from human demonstrations can facilitate automation but is risk...

0 Jonathan Lee, et al. ∙

∙ 01/31/2018

Derivative-Free Failure Avoidance Control for Manipulation using Learned Support Constraints

Learning to accomplish tasks such as driving, grasping or surgery from s...

0 Jonathan Lee, et al. ∙

∙ 12/26/2017

Ray RLLib: A Composable and Scalable Reinforcement Learning Library

Reinforcement learning (RL) algorithms involve the deep nesting of disti...

1 Eric Liang, et al. ∙

∙ 10/15/2017

DDCO: Discovery of Deep Continuous Options for Robot Learning from Demonstrations

An option is a short-term skill consisting of a control policy for a spe...

0 Sanjay Krishnan, et al. ∙

∙ 09/18/2016

Principled Option Learning in Markov Decision Processes

It is well known that options can make planning more efficient, among th...

0 Roy Fox, et al. ∙

∙ 06/27/2012

Bounded Planning in Passive POMDPs

In Passive POMDPs actions do not affect the world state, but still incur...

0 Roy Fox, et al. ∙