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 3951–3975 of 15113 papers

TitleStatusHype
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers—0
Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning—0
Highway Graph to Accelerate Reinforcement LearningCode0
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls—0
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement LearningCode0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Large Language Models are Biased Reinforcement LearnersCode0
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning—0
Comparisons Are All You Need for Optimizing Smooth Functions—0
Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses—0
Combined film and pulse heating of lithium ion batteries to improve performance in low ambient temperature—0
Optimal control barrier functions for RL based safe powertrain control—0
LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions—0
Stochastic Q-learning for Large Discrete Action Spaces—0
Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail PromotionsCode0
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning—0
IM-RAG: Multi-Round Retrieval-Augmented Generation Through Learning Inner Monologues—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Fast Two-Time-Scale Stochastic Gradient Method with Applications in Reinforcement Learning—0
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
Neural Network Compression for Reinforcement Learning Tasks—0
Intrinsic Rewards for Exploration without Harm from Observational Noise: A Simulation Study Based on the Free Energy Principle—0
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Benchmark Results

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