Continuous Control

407 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

PRISE: Learning Temporal Action Abstractions as a Sequence Compression Problem

frankzheng2022/prise 16 Feb 2024

To do so, we bring a subtle but critical component of LLM training pipelines -- input tokenization via byte pair encoding (BPE) -- to the seemingly distant task of learning skills of variable time span in continuous control domains.

6
16 Feb 2024

Dataset Clustering for Improved Offline Policy Learning

wq13552463699/clustering-for-offline-rl-il 14 Feb 2024

Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment.

0
14 Feb 2024

Hybrid Inverse Reinforcement Learning

jren03/garage 13 Feb 2024

In this work, we propose using hybrid RL -- training on a mixture of online and expert data -- to curtail unnecessary exploration.

2
13 Feb 2024

Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss

premiertaco/premier-taco 9 Feb 2024

We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.

4
09 Feb 2024

FlowPG: Action-constrained Policy Gradient with Normalizing Flows

rlr-smu/flow-pg NeurIPS 2023

To address this, first we use a normalizing flow model to learn an invertible, differentiable mapping between the feasible action space and the support of a simple distribution on a latent variable, such as Gaussian.

0
07 Feb 2024

The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations

matt00n/policygradientsjax 24 Jan 2024

In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning.

9
24 Jan 2024

Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System

han-adam/generalanalyze 16 Dec 2023

Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment.

1
16 Dec 2023

Risk-Aware Continuous Control with Neural Contextual Bandits

jaayala/risk_aware_contextual_bandit 15 Dec 2023

Recent advances in learning techniques have garnered attention for their applicability to a diverse range of real-world sequential decision-making problems.

0
15 Dec 2023

World Models via Policy-Guided Trajectory Diffusion

marc-rigter/polygrad-world-models 13 Dec 2023

Our results demonstrate that PolyGRAD outperforms state-of-the-art baselines in terms of trajectory prediction error for short trajectories, with the exception of autoregressive diffusion.

33
13 Dec 2023

Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills

hehongc/DCMRL 11 Dec 2023

We propose a framework called decoupled meta-reinforcement learning (DCMRL), which (1) contrastively restricts the learning of task contexts through pulling in similar task contexts within the same task and pushing away different task contexts of different tasks, and (2) utilizes a Gaussian quantization variational autoencoder (GQ-VAE) for clustering the Gaussian distributions of the task contexts and skills respectively, and decoupling the exploration and learning processes of their spaces.

3
11 Dec 2023