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 6851–6900 of 15113 papers

TitleStatusHype
HARPO: Learning to Subvert Online Behavioral Advertising—0
Safe Policy Optimization with Local Generalized Linear Function ApproximationsCode0
Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning—0
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
Dueling RL: Reinforcement Learning with Trajectory Preferences—0
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods—0
Reinforcement Learning for Mixed Autonomy IntersectionsCode1
A Dataset Perspective on Offline Reinforcement LearningCode1
Interactive Inverse Reinforcement Learning for Cooperative Games—0
Batch Reinforcement Learning from Crowds—0
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance—0
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance—0
Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach—0
Automatic Goal Generation using Dynamical Distance Learning—0
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field ExperimentsCode1
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning—0
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks—0
d3rlpy: An Offline Deep Reinforcement Learning LibraryCode0
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning—0
A Deep Reinforcement Learning Approach for Composing Moving IoT Services—0
Development of collective behavior in newborn artificial agents—0
Robust Deep Reinforcement Learning for Quadcopter ControlCode1
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space—0
Supervised Advantage Actor-Critic for Recommender Systems—0
Improving RNA Secondary Structure Design using Deep Reinforcement Learning—0
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