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 5051–5075 of 15113 papers

TitleStatusHype
How to Use Reinforcement Learning to Facilitate Future Electricity Market Design? Part 1: A Paradigmatic Theory—0
Simple Noisy Environment Augmentation for Reinforcement LearningCode0
Rethinking Population-assisted Off-policy Reinforcement Learning—0
Sim2Rec: A Simulator-based Decision-making Approach to Optimize Real-World Long-term User Engagement in Sequential Recommender SystemsCode0
Gym-preCICE: Reinforcement Learning Environments for Active Flow Control—0
An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options FrameworkCode0
Validation of massively-parallel adaptive testing using dynamic control matching—0
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality GuaranteesCode0
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings—0
Joint Learning of Policy with Unknown Temporal Constraints for Safe Reinforcement Learning—0
A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks—0
One-Step Distributional Reinforcement Learning—0
Reinforcement Learning with Partial Parametric Model Knowledge—0
Multi-criteria Hardware Trojan Detection: A Reinforcement Learning Approach—0
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation ProcessingCode0
Distance Weighted Supervised Learning for Offline Interaction DataCode0
Can Agents Run Relay Race with Strangers? Generalization of RL to Out-of-Distribution TrajectoriesCode0
A Closer Look at Reward Decomposition for High-Level Robotic Explanations—0
Loss- and Reward-Weighting for Efficient Distributed Reinforcement Learning—0
Proximal Curriculum for Reinforcement Learning AgentsCode0
Model Extraction Attacks Against Reinforcement Learning Based Controllers—0
What can online reinforcement learning with function approximation benefit from general coverage conditions?—0
On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes—0
Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey—0
Policy Resilience to Environment Poisoning Attacks on Reinforcement Learning—0
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Benchmark Results

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