Continuous Control

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.

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

Proximal Policy Optimization Algorithms

Content

Continuous control with deep reinforcement learning

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

Addressing Function Approximation Error in Actor-Critic Methods

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

Simple random search provides a competitive approach to reinforcement learning

Dream to Control: Learning Behaviors by Latent Imagination

High-Dimensional Continuous Control Using Generalized Advantage Estimation

Benchmarking Deep Reinforcement Learning for Continuous Control

Conservative Q-Learning for Offline Reinforcement Learning