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Continuous Control

290 papers with code • 73 benchmarks • 7 datasets

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Libraries

Use these libraries to find Continuous Control models and implementations
DLR-RM/stable-baselines3
6 papers
3,959
hill-a/stable-baselines
5 papers
3,596
takuseno/d3rlpy
5 papers
685
toni-sm/skrl
3 papers
58
See all 17 libraries.

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Continuous control with deep reinforcement learning

ray-project/ray • 9 Sep 2015

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.

145
Paper
Code

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

haarnoja/sac • • ICML 2018

A platform for Applied Reinforcement Learning (Applied RL)

61
Paper
Code

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

DartEnv/dart-env • • 26 Feb 2018

The purpose of this technical report is two-fold.

30
Paper
Code

Simple random search provides a competitive approach to reinforcement learning

modestyachts/ARS • 19 Mar 2018

A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions.

25
Paper
Code

High-Dimensional Continuous Control Using Generalized Advantage Estimation

labmlai/annotated_deep_learning_paper_implementations • • 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.

17
Paper
Code

Benchmarking Deep Reinforcement Learning for Continuous Control

rllab/rllab • • 22 Apr 2016

Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.

15
Paper
Code

Dream to Control: Learning Behaviors by Latent Imagination

danijar/dreamer • • ICLR 2020

Learned world models summarize an agent's experience to facilitate learning complex behaviors.

12
Paper
Code

Conservative Q-Learning for Offline Reinforcement Learning

aviralkumar2907/CQL • • NeurIPS 2020

We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees.

11
Paper
Code

Parameter Space Noise for Exploration

tensorflow/models • • ICLR 2018

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

10
Paper
Code

Off-Policy Deep Reinforcement Learning without Exploration

sfujim/BCQ • • 7 Dec 2018

Many practical applications of reinforcement learning constrain agents to learn from a fixed batch of data which has already been gathered, without offering further possibility for data collection.

10
Paper
Code
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