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 651–675 of 15113 papers

TitleStatusHype
Reinforcement Learning with Maskable Stock Representation for Portfolio Management in Customizable Stock PoolsCode1
Reinforcement Learning with Model Predictive Control for Highway Ramp MeteringCode1
Direct Preference Optimization for Neural Machine Translation with Minimum Bayes Risk DecodingCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Accelerating Exploration with Unlabeled Prior DataCode1
Uni-O4: Unifying Online and Offline Deep Reinforcement Learning with Multi-Step On-Policy OptimizationCode1
Hierarchical Reinforcement Learning for Power Network Topology ControlCode1
State-Wise Safe Reinforcement Learning With Pixel ObservationsCode1
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction EstimationCode1
Unleashing the Power of Pre-trained Language Models for Offline Reinforcement LearningCode1
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of SkillsCode1
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio MinimizationCode1
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement LearningCode1
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLACode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
Contrastive Preference Learning: Learning from Human Feedback without RLCode1
Towards Robust Offline Reinforcement Learning under Diverse Data CorruptionCode1
Vision-Language Models are Zero-Shot Reward Models for Reinforcement LearningCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
AMAGO: Scalable In-Context Reinforcement Learning for Adaptive AgentsCode1
Reduced Policy Optimization for Continuous Control with Hard ConstraintsCode1
METRA: Scalable Unsupervised RL with Metric-Aware AbstractionCode1
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration BiasCode1
Show:102550
← PrevPage 27 of 605Next →

Benchmark Results

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