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 851–875 of 15113 papers

TitleStatusHype
Multimodal Dreaming: A Global Workspace Approach to World Model-Based Reinforcement Learning—0
Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning—0
On the Importance of Reward Design in Reinforcement Learning-based Dynamic Algorithm Configuration: A Case Study on OneMax with (1+(λ,λ))-GACode0
R1-T1: Fully Incentivizing Translation Capability in LLMs via Reasoning Learning—0
CarPlanner: Consistent Auto-regressive Trajectory Planning for Large-scale Reinforcement Learning in Autonomous Driving—0
Accelerating Model-Based Reinforcement Learning with State-Space World Models—0
Improving the Efficiency of a Deep Reinforcement Learning-Based Power Management System for HPC Clusters Using Curriculum Learning—0
AutoBS: Autonomous Base Station Deployment with Reinforcement Learning and Digital Network TwinsCode0
VEM: Environment-Free Exploration for Training GUI Agent with Value Environment ModelCode1
WOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management StrategiesCode0
Distilling Reinforcement Learning Algorithms for In-Context Model-Based PlanningCode1
Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?—0
Efficient Reinforcement Learning by Guiding Generalist World Models with Non-Curated Data—0
Error-related Potential driven Reinforcement Learning for adaptive Brain-Computer Interfaces—0
Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement LearningCode0
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution—0
FetchBot: Object Fetching in Cluttered Shelves via Zero-Shot Sim2Real—0
Yes, Q-learning Helps Offline In-Context RL—0
Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach—0
From Perceptions to Decisions: Wildfire Evacuation Decision Prediction with Behavioral Theory-informed LLMsCode0
Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning—0
TDMPBC: Self-Imitative Reinforcement Learning for Humanoid Robot ControlCode4
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language ModelsCode2
Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation—0
Ensemble RL through Classifier Models: Enhancing Risk-Return Trade-offs in Trading Strategies—0
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Benchmark Results

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