Playing Games
 
Edit

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

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
Paper
Code

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
Paper
Code

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.

39
13 Dec 2023
Paper
Code

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
Paper
Code

DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization

XuGW-Kevin/DrM • • 30 Oct 2023

To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.

38
30 Oct 2023
Paper
Code

TD-MPC2: Scalable, Robust World Models for Continuous Control

nicklashansen/tdmpc2 • • 25 Oct 2023

TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model.

208
25 Oct 2023
Paper
Code

Absolute Policy Optimization

intelligent-control-lab/absolute-policy-optimization • • 20 Oct 2023

In recent years, trust region on-policy reinforcement learning has achieved impressive results in addressing complex control tasks and gaming scenarios.

2
20 Oct 2023
Paper
Code

Reduced Policy Optimization for Continuous Control with Hard Constraints

wadx2019/rpo • • NeurIPS 2023

To the best of our knowledge, RPO is the first attempt that introduces GRG to RL as a way of efficiently handling both equality and inequality hard constraints.

14
14 Oct 2023
Paper
Code

Boosting Continuous Control with Consistency Policy

cccedric/cpql • • 10 Oct 2023

By establishing a mapping from the reverse diffusion trajectories to the desired policy, we simultaneously address the issues of time efficiency and inaccurate guidance when updating diffusion model-based policy with the learned Q-function.

16
10 Oct 2023
Paper
Code

Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control

nathanrahn/return-landscapes • • NeurIPS 2023

To conclude, we develop a distribution-aware procedure which finds such paths, navigating away from noisy neighborhoods in order to improve the robustness of a policy.

6
26 Sep 2023
Paper
Code
Contact us on: hello@paperswithcode.com . Papers With Code is a free resource with all data licensed under CC-BY-SA.
Terms Data policy Cookies policy