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 7101–7125 of 15113 papers

TitleStatusHype
Learning Temporally-Consistent Representations for Data-Efficient Reinforcement LearningCode0
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Bid Optimization using Maximum Entropy Reinforcement Learning—0
Learning a subspace of policies for online adaptation in Reinforcement Learning—0
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
Safe Reinforcement Learning Using Robust Control Barrier FunctionsCode1
Understanding the Safety Requirements for Learning-based Power Systems OperationsCode0
REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents—0
Reinforcement Learning for Systematic FX Trading—0
Multi-condition multi-objective optimization using deep reinforcement learning—0
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Hard instance learning for quantum adiabatic prime factorization—0
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization—0
An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots—0
MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement LearningCode1
Reinforcement Learning In Two Player Zero Sum Simultaneous Action GamesCode0
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning—0
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning—0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
A MultiModal Social Robot Toward Personalized Emotion Interaction—0
Training Transition Policies via Distribution Matching for Complex TasksCode0
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Benchmark Results

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