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 2351–2375 of 15113 papers

TitleStatusHype
Koopman-Assisted Reinforcement Learning—0
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Improving the Validity of Automatically Generated Feedback via Reinforcement LearningCode1
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
Robust Policy Learning via Offline Skill Diffusion—0
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks—0
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning—0
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning—0
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
Offline Fictitious Self-Play for Competitive Games—0
Curiosity-driven Red-teaming for Large Language ModelsCode2
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
ArCHer: Training Language Model Agents via Hierarchical Multi-Turn RLCode3
RL-GPT: Integrating Reinforcement Learning and Code-as-policy—0
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment—0
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis UseCode0
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning—0
Learning to Program Variational Quantum Circuits with Fast Weights—0
Flexible Robust Beamforming for Multibeam Satellite Downlink using Reinforcement LearningCode1
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test—0
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement LearningCode3
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
QF-tuner: Breaking Tradition in Reinforcement Learning—0
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Benchmark Results

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