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Continuous Control

422 papers with code • 73 benchmarks • 10 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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Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning

no code yet • 4 Mar 2024

It has been observed that this scheme can be potentially generalized to carry out multiple iterations of the Bellman operator at once, benefiting the underlying learning algorithm.

 
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EfficientZero V2: Mastering Discrete and Continuous Control with Limited Data

no code yet • 1 Mar 2024

We have expanded the performance of EfficientZero to multiple domains, encompassing both continuous and discrete actions, as well as visual and low-dimensional inputs.

 
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A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations

no code yet • 29 Feb 2024

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency.

 
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DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning

no code yet • 25 Feb 2024

We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates.

 
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ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization

no code yet • 22 Feb 2024

The varying significance of distinct primitive behaviors during the policy learning process has been overlooked by prior model-free RL algorithms.

 
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Exploiting Estimation Bias in Deep Double Q-Learning for Actor-Critic Methods

no code yet • 14 Feb 2024

This paper introduces innovative methods in Reinforcement Learning (RL), focusing on addressing and exploiting estimation biases in Actor-Critic methods for continuous control tasks, using Deep Double Q-Learning.

 
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Offline Actor-Critic Reinforcement Learning Scales to Large Models

no code yet • 8 Feb 2024

We show that offline actor-critic reinforcement learning can scale to large models - such as transformers - and follows similar scaling laws as supervised learning.

 
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Reinforcement Learning as a Catalyst for Robust and Fair Federated Learning: Deciphering the Dynamics of Client Contributions

no code yet • 8 Feb 2024

Recent advancements in federated learning (FL) have produced models that retain user privacy by training across multiple decentralized devices or systems holding local data samples.

 
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Learning Diverse Policies with Soft Self-Generated Guidance

no code yet • 7 Feb 2024

However, existing methods often require these experiences to be successful and may overly exploit them, which can cause the agent to adopt suboptimal behaviors.

 
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Understanding What Affects Generalization Gap in Visual Reinforcement Learning: Theory and Empirical Evidence

no code yet • 5 Feb 2024

Recently, there are many efforts attempting to learn useful policies for continuous control in visual reinforcement learning (RL).

 
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