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 726–750 of 15113 papers

TitleStatusHype
Autonomous Radiotherapy Treatment Planning Using DOLA: A Privacy-Preserving, LLM-Based Optimization Agent—0
Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models—0
OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning—0
Think or Not Think: A Study of Explicit Thinking in Rule-Based Visual Reinforcement Fine-TuningCode2
Reinforcement Learning-based Heuristics to Guide Domain-Independent Dynamic ProgrammingCode0
Grammar and Gameplay-aligned RL for Game Description Generation with LLMs—0
Reinforcement Learning for Reasoning in Small LLMs: What Works and What Doesn'tCode3
RL4Med-DDPO: Reinforcement Learning for Controlled Guidance Towards Diverse Medical Image Generation using Vision-Language Foundation Models—0
Stop Overthinking: A Survey on Efficient Reasoning for Large Language ModelsCode4
Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement LearningCode4
UAS Visual Navigation in Large and Unseen Environments via a Meta Agent—0
Comprehensive Review of Reinforcement Learning for Medical Ultrasound Imaging—0
Behaviour Discovery and Attribution for Explainable Reinforcement Learning—0
Reinforcement Learning Environment with LLM-Controlled Adversary in D&D 5th Edition Combat—0
Good Actions Succeed, Bad Actions Generalize: A Case Study on Why RL Generalizes Better—0
Empowering Medical Multi-Agents with Clinical Consultation Flow for Dynamic Diagnosis—0
DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning—0
LogLLaMA: Transformer-based log anomaly detection with LLaMA—0
1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities—0
Reward Training Wheels: Adaptive Auxiliary Rewards for Robotics Reinforcement Learning—0
Neural Lyapunov Function Approximation with Self-Supervised Reinforcement LearningCode0
Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor controlCode2
Cosmos-Reason1: From Physical Common Sense To Embodied ReasoningCode4
CTSAC: Curriculum-Based Transformer Soft Actor-Critic for Goal-Oriented Robot Exploration—0
Pauli Network Circuit Synthesis with Reinforcement Learning—0
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Benchmark Results

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