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 9951–9975 of 15113 papers

TitleStatusHype
Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs—0
Flightmare: A Flexible Quadrotor SimulatorCode2
Data-driven Outer-Loop Control Using Deep Reinforcement Learning for Trajectory Tracking—0
Beyond variance reduction: Understanding the true impact of baselines on policy optimization—0
Control of a Nature-inspired Scorpion using Reinforcement Learning—0
Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL—0
Ranking Policy DecisionsCode0
Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives—0
Human-in-the-Loop Methods for Data-Driven and Reinforcement Learning Systems—0
How does the structure embedded in learning policy affect learning quadruped locomotion?—0
Reinforcement Learning with Feedback-modulated TD-STDP—0
Real-world Video Adaptation with Reinforcement Learning—0
Sample Efficiency in Sparse Reinforcement Learning: Or Your Money Back—0
Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles—0
On the model-based stochastic value gradient for continuous reinforcement learningCode1
Query Focused Multi-document Summarisation of Biomedical Texts: Macquarie Universiy and the Australian National University at BioASQ8bCode0
Market-making with reinforcement-learning (SAC)Code1
Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning—0
AutoFS: Automated Feature Selection via Diversity-aware Interactive Reinforcement LearningCode0
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI—0
The Advantage Regret-Matching Actor-Critic—0
Query Focused Multi-document Summarisation of Biomedical TextsCode0
Selective Particle Attention: Visual Feature-Based Attention in Deep Reinforcement Learning—0
Synthetic Sample Selection via Reinforcement Learning—0
Identifying Critical States by the Action-Based Variance of Expected Return—0
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Benchmark Results

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