Abbas Abdolmaleki

research

∙ 08/29/2023

Policy composition in reinforcement learning via multi-objective policy optimization

We enable reinforcement learning agents to learn successful behavior pol...

0 Shruti Mishra, et al. ∙

research

∙ 06/20/2023

RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation

The ability to leverage heterogeneous robotic experience from different ...

0 Konstantinos Bousmalis, et al. ∙

research

∙ 02/24/2023

Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains

In this paper we study the problem of learning multi-step dynamics predi...

0 Jingwei Zhang, et al. ∙

research

∙ 11/24/2022

SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration

The ability to effectively reuse prior knowledge is a key requirement wh...

0 Giulia Vezzani, et al. ∙

research

∙ 05/06/2022

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation

Reinforcement learning (RL) has been shown to be effective at learning c...

0 Alex X. Lee, et al. ∙

research

∙ 04/21/2022

Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach

Actor-critic algorithms that make use of distributional policy evaluatio...

0 Bobak Shahriari, et al. ∙

research

∙ 04/12/2022

Offline Distillation for Robot Lifelong Learning with Imbalanced Experience

Robots will experience non-stationary environment dynamics throughout th...

0 Wenxuan Zhou, et al. ∙

research

∙ 10/12/2021

Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes

We study the problem of robotic stacking with objects of complex geometr...

0 Alex X. Lee, et al. ∙

research

∙ 10/07/2021

Evaluating model-based planning and planner amortization for continuous control

There is a widespread intuition that model-based control methods should ...

0 Arunkumar Byravan, et al. ∙

research

∙ 06/15/2021

On Multi-objective Policy Optimization as a Tool for Reinforcement Learning

Many advances that have improved the robustness and efficiency of deep r...

0 Abbas Abdolmaleki, et al. ∙

research

∙ 05/25/2021

From Motor Control to Team Play in Simulated Humanoid Football

Intelligent behaviour in the physical world exhibits structure at multip...

2 Siqi Liu, et al. ∙

research

∙ 01/23/2021

Rethinking Exploration for Sample-Efficient Policy Learning

Off-policy reinforcement learning for control has made great strides in ...

0 William F. Whitney, et al. ∙

research

∙ 10/29/2020

"What, not how": Solving an under-actuated insertion task from scratch

Robot manipulation requires a complex set of skills that need to be care...

0 Giulia Vezzani, et al. ∙

research

∙ 10/12/2020

Local Search for Policy Iteration in Continuous Control

We present an algorithm for local, regularized, policy improvement in re...

0 Jost Tobias Springenberg, et al. ∙

research

∙ 07/30/2020

Data-efficient Hindsight Off-policy Option Learning

Solutions to most complex tasks can be decomposed into simpler, intermed...

38 Markus Wulfmeier, et al. ∙

research

∙ 06/01/2020

Acme: A Research Framework for Distributed Reinforcement Learning

Deep reinforcement learning has led to many recent-and groundbreaking-ad...

22 Matt Hoffman, et al. ∙

research

∙ 05/15/2020

A Distributional View on Multi-Objective Policy Optimization

Many real-world problems require trading off multiple competing objectiv...

0 Abbas Abdolmaleki, et al. ∙

research

∙ 02/19/2020

Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning

Off-policy reinforcement learning algorithms promise to be applicable in...

7 Noah Y. Siegel, et al. ∙

research

∙ 01/02/2020

Continuous-Discrete Reinforcement Learning for Hybrid Control in Robotics

Many real-world control problems involve both discrete decision variable...

15 Michael Neunert, et al. ∙

research

∙ 11/05/2019

Quinoa: a Q-function You Infer Normalized Over Actions

We present an algorithm for learning an approximate action-value soft Q-...

20 Jonas Degrave, et al. ∙

research

∙ 10/21/2019

Modelling Generalized Forces with Reinforcement Learning for Sim-to-Real Transfer

Learning robotic control policies in the real world gives rise to challe...

0 Rae Jeong, et al. ∙

research

∙ 10/09/2019

Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models

Humans are masters at quickly learning many complex tasks, relying on an...

5 Arunkumar Byravan, et al. ∙

research

∙ 10/01/2019

Augmenting learning using symmetry in a biologically-inspired domain

Invariances to translation, rotation and other spatial transformations a...

0 Shruti Mishra, et al. ∙

research

∙ 09/26/2019

V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control

Some of the most successful applications of deep reinforcement learning ...

0 H. Francis Song, et al. ∙

research

∙ 06/26/2019

Regularized Hierarchical Policies for Compositional Transfer in Robotics

The successful application of flexible, general learning algorithms -- s...

1 Markus Wulfmeier, et al. ∙

research

∙ 06/18/2019

Robust Reinforcement Learning for Continuous Control with Model Misspecification

We provide a framework for incorporating robustness -- to perturbations ...

0 Daniel J. Mankowitz, et al. ∙

research

∙ 02/13/2019

Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup

We present a method for fast training of vision based control policies o...

2 Devin Schwab, et al. ∙

research

∙ 02/12/2019

Value constrained model-free continuous control

The naive application of Reinforcement Learning algorithms to continuous...

0 Steven Bohez, et al. ∙

research

∙ 12/05/2018

Relative Entropy Regularized Policy Iteration

We present an off-policy actor-critic algorithm for Reinforcement Learni...

2 Abbas Abdolmaleki, et al. ∙

research

∙ 06/14/2018

Maximum a Posteriori Policy Optimisation

We introduce a new algorithm for reinforcement learning called Maximum a...

0 Abbas Abdolmaleki, et al. ∙

research

∙ 01/02/2018

DeepMind Control Suite

The DeepMind Control Suite is a set of continuous control tasks with a s...

0 Yuval Tassa, et al. ∙

research

∙ 05/22/2017

Deep Reinforcement Learning with Relative Entropy Stochastic Search

Many reinforcement learning methods for continuous control tasks are bas...

0 Voot Tangkaratt, et al. ∙

Abbas Abdolmaleki

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