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.
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Latest papers
PRISE: Learning Temporal Action Abstractions as a Sequence Compression Problem
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.
Dataset Clustering for Improved Offline Policy Learning
Offline policy learning aims to discover decision-making policies from previously-collected datasets without additional online interactions with the environment.
Hybrid Inverse Reinforcement Learning
In this work, we propose using hybrid RL -- training on a mixture of online and expert data -- to curtail unnecessary exploration.
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
FlowPG: Action-constrained Policy Gradient with Normalizing Flows
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.
The Definitive Guide to Policy Gradients in Deep Reinforcement Learning: Theory, Algorithms and Implementations
In recent years, various powerful policy gradient algorithms have been proposed in deep reinforcement learning.
Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System
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.
Risk-Aware Continuous Control with Neural Contextual Bandits
Recent advances in learning techniques have garnered attention for their applicability to a diverse range of real-world sequential decision-making problems.
World Models via Policy-Guided Trajectory Diffusion
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.
Decoupling Meta-Reinforcement Learning with Gaussian Task Contexts and Skills
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.