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Continuous Control

413 papers with code • 73 benchmarks • 9 datasets

Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.

Libraries

Use these libraries to find Continuous Control models and implementations

Datasets

Subtasks

Most implemented papers

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Parameter Space Noise for Exploration

tensorflow/models • • ICLR 2018

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

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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.

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Controlling Overestimation Bias with Truncated Mixture of Continuous Distributional Quantile Critics

bayesgroup/tqc_pytorch • • ICML 2020

The overestimation bias is one of the major impediments to accurate off-policy learning.

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Learning Latent Dynamics for Planning from Pixels

google-research/planet • • 12 Nov 2018

Planning has been very successful for control tasks with known environment dynamics.

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Continuous Deep Q-Learning with Model-based Acceleration

jakegrigsby/deep_control • • 2 Mar 2016

In this paper, we explore algorithms and representations to reduce the sample complexity of deep reinforcement learning for continuous control tasks.

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Sample Efficient Actor-Critic with Experience Replay

hill-a/stable-baselines • • 3 Nov 2016

This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems.

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Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

openai/baselines • • NeurIPS 2017

In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature.

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DeepMind Control Suite

deepmind/dm_control • 2 Jan 2018

The DeepMind Control Suite is a set of continuous control tasks with a standardised structure and interpretable rewards, intended to serve as performance benchmarks for reinforcement learning agents.

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Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model

alexlee-gk/slac • • NeurIPS 2020

Deep reinforcement learning (RL) algorithms can use high-capacity deep networks to learn directly from image observations.

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Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

facebookresearch/drqv2 • • ICLR 2022

We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control.

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