Continuous Control

208 papers with code • 73 benchmarks • 7 datasets

This task has no description! Would you like to contribute one?

Greatest papers with code

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

tensorflow/models NeurIPS 2018

Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity.

Continuous Control

Trust-PCL: An Off-Policy Trust Region Method for Continuous Control

tensorflow/models ICLR 2018

When evaluated on a number of continuous control tasks, Trust-PCL improves the solution quality and sample efficiency of TRPO.

Continuous Control

Parameter Space Noise for Exploration

tensorflow/models ICLR 2018

Combining parameter noise with traditional RL methods allows to combine the best of both worlds.

Continuous Control

Primal Wasserstein Imitation Learning

google-research/google-research ICLR 2021

Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert.

Continuous Control Imitation Learning

Behavior Regularized Offline Reinforcement Learning

google-research/google-research 26 Nov 2019

In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment.

Continuous Control Offline RL

Unsupervised Learning of Object Structure and Dynamics from Videos

google-research/google-research NeurIPS 2019

Extracting and predicting object structure and dynamics from videos without supervision is a major challenge in machine learning.

Action Recognition Continuous Control +2

Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning

google/trax 1 Oct 2019

In this paper, we aim to develop a simple and scalable reinforcement learning algorithm that uses standard supervised learning methods as subroutines.

Continuous Control OpenAI Gym

High-Dimensional Continuous Control Using Generalized Advantage Estimation

lab-ml/nn 8 Jun 2015

Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.

Continuous Control Policy Gradient Methods