Playing Games
 
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Continuous Control

415 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

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Datasets

Subtasks

Most implemented papers

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Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

tensorflow/models • • ICLR 2019

We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.

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CURL: Contrastive Unsupervised Representations for Reinforcement Learning

MishaLaskin/curl • • 8 Apr 2020

On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features.

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Evolution-Guided Policy Gradient in Reinforcement Learning

ShawK91/erl_paper_nips18 • • NeurIPS 2018

However, these methods typically suffer from three core difficulties: temporal credit assignment with sparse rewards, lack of effective exploration, and brittle convergence properties that are extremely sensitive to hyperparameters.

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MOPO: Model-based Offline Policy Optimization

tianheyu927/mopo • NeurIPS 2020

We also characterize the trade-off between the gain and risk of leaving the support of the batch data.

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Action Branching Architectures for Deep Reinforcement Learning

atavakol/action-branching-agents • • 24 Nov 2017

This approach achieves a linear increase of the number of network outputs with the number of degrees of freedom by allowing a level of independence for each individual action dimension.

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Distributed Distributional Deterministic Policy Gradients

opendilab/DI-engine • • ICLR 2018

This work adopts the very successful distributional perspective on reinforcement learning and adapts it to the continuous control setting.

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Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

reinforcement-learning-kr/lets-do-irl • • ICLR 2019

By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.

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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.

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IQ-Learn: Inverse soft-Q Learning for Imitation

Div99/IQ-Learn • • NeurIPS 2021

In many sequential decision-making problems (e. g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task.

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Deep Reinforcement Learning that Matters

chainer/chainerrl • • 19 Sep 2017

In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL).

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