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 951–975 of 15113 papers

TitleStatusHype
Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning—0
Enhancing Pre-Trained Decision Transformers with Prompt-Tuning Bandits—0
Adversarially-Robust TD Learning with Markovian Data: Finite-Time Rates and Fundamental Limits—0
DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM GuardrailsCode1
Convergent NMPC-based Reinforcement Learning Using Deep Expected Sarsa and Nonlinear Temporal Difference Learning—0
LLM Alignment as Retriever Optimization: An Information Retrieval Perspective—0
Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning—0
Mirror Descent Actor Critic via Bounded Advantage Learning—0
Illuminating Spaces: Deep Reinforcement Learning and Laser-Wall Partitioning for Architectural Layout Generation—0
Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs—0
Transforming Multimodal Models into Action Models for Radiotherapy—0
Autotelic Reinforcement Learning: Exploring Intrinsic Motivations for Skill Acquisition in Open-Ended Environments—0
Training Language Models to Reason EfficientlyCode2
CTR-Driven Advertising Image Generation with Multimodal Large Language ModelsCode2
Demystifying Long Chain-of-Thought Reasoning in LLMsCode3
OmniRL: In-Context Reinforcement Learning by Large-Scale Meta-Training in Randomized Worlds—0
Underwater Soft Fin Flapping Motion with Deep Neural Network Based Surrogate ModelCode0
Optimizing Electric Vehicles Charging using Large Language Models and Graph Neural Networks—0
AI-driven materials design: a mini-review—0
Calibrated Unsupervised Anomaly Detection in Multivariate Time-series using Reinforcement Learning—0
Reusing Embeddings: Reproducible Reward Model Research in Large Language Model Alignment without GPUsCode2
Adviser-Actor-Critic: Eliminating Steady-State Error in Reinforcement Learning Control—0
Brief analysis of DeepSeek R1 and it's implications for Generative AI—0
Flow Q-LearningCode3
RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for Vision-Based Drone Navigation—0
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Benchmark Results

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