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 2776–2800 of 15113 papers

TitleStatusHype
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
Reinforcement Learning-based Token Pruning in Vision Transformers: A Markov Game ApproachCode0
Reinforcement Learning for Active Matter—0
RL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations—0
Reasoning-SQL: Reinforcement Learning with SQL Tailored Partial Rewards for Reasoning-Enhanced Text-to-SQL—0
Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning—0
Entropy-guided sequence weighting for efficient exploration in RL-based LLM fine-tuning—0
FLAM: Foundation Model-Based Body Stabilization for Humanoid Locomotion and Manipulation—0
Reinforcement Learning for Machine Learning Model Deployment: Evaluating Multi-Armed Bandits in ML Ops Environments—0
Controlling Large Language Model with Latent ActionsCode0
Pretrained Bayesian Non-parametric Knowledge Prior in Robotic Long-Horizon Reinforcement LearningCode0
Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks—0
Reward Design for Reinforcement Learning AgentsCode0
Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal ControlCode0
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation—0
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning—0
Synthesizing world models for bilevel planning—0
Reasoning Beyond Limits: Advances and Open Problems for LLMs—0
Offline Reinforcement Learning with Discrete Diffusion Skills—0
Learning Adaptive Dexterous Grasping from Single Demonstrations—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking—0
Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer LearningCode0
Mining-Gym: A Configurable RL Benchmarking Environment for Truck Dispatch SchedulingCode0
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Benchmark Results

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