SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6876–6900 of 15113 papers

TitleStatusHype
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning—0
An Algorithmic Theory of Metacognition in Minds and Machines—0
Control of a fly-mimicking flyer in complex flow using deep reinforcement learning—0
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
Successor Feature Neural Episodic Control—0
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning—0
Model-Free Risk-Sensitive Reinforcement Learning—0
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement LearningCode1
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning—0
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning—0
Imagine Networks—0
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles—0
B-Pref: Benchmarking Preference-Based Reinforcement LearningCode1
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies—0
Autonomous Attack Mitigation for Industrial Control Systems—0
AlphaD3M: Machine Learning Pipeline Synthesis—0
Online Service Provisioning in NFV-enabled Networks Using Deep Reinforcement Learning—0
Tuning the Weights: The Impact of Initial Matrix Configurations on Successor Features Learning Efficacy—0
What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks—0
Model-Based Episodic Memory Induces Dynamic Hybrid Controls—0
Smooth Imitation Learning via Smooth Costs and Smooth Policies—0
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework—0
Curriculum Offline Imitation LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified