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

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Showing 7676–7700 of 15113 papers

TitleStatusHype
Representation and Invariance in Reinforcement Learning—0
Meta-CPR: Generalize to Unseen Large Number of Agents with Communication Pattern Recognition Module—0
Reinforcing Semantic-Symmetry for Document Summarization—0
Programmatic Reward Design by Example—0
Teaching a Robot to Walk Using Reinforcement Learning—0
A Benchmark for Low-Switching-Cost Reinforcement Learning—0
Continual Learning In Environments With Polynomial Mixing TimesCode0
Contextual Exploration Using a Linear Approximation Method Based on Satisficing—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Formalising the Foundations of Discrete Reinforcement Learning in Isabelle/HOL—0
Federated Reinforcement Learning at the Edge—0
MedAttacker: Exploring Black-Box Adversarial Attacks on Risk Prediction Models in Healthcare—0
Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots—0
Quantum Architecture Search via Continual Reinforcement Learning—0
A Validation Tool for Designing Reinforcement Learning Environments—0
How Private Is Your RL Policy? An Inverse RL Based Analysis FrameworkCode0
Blockwise Sequential Model Learning for Partially Observable Reinforcement LearningCode0
Encoding priors in the brain: a reinforcement learning model for mouse decision making—0
Edge-Compatible Reinforcement Learning for Recommendations—0
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning—0
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization—0
Reinforcement Learning with Almost Sure Constraints—0
Cooperative Multi-Agent Reinforcement Learning with Hypergraph ConvolutionCode0
Recent Advances in Reinforcement Learning in Finance—0
Suboptimal and trait-like reinforcement learning strategies correlate with midbrain encoding of prediction errors—0
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Benchmark Results

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