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 1651–1675 of 15113 papers

TitleStatusHype
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate ModelsCode0
Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach—0
Optimization Solution Functions as Deterministic Policies for Offline Reinforcement Learning—0
Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper—0
Learning Robust Reward Machines from Noisy LabelsCode0
Unsupervised-to-Online Reinforcement Learning—0
MiWaves Reinforcement Learning AlgorithmCode0
Simultaneous Training of First- and Second-Order Optimizers in Population-Based Reinforcement Learning—0
What makes math problems hard for reinforcement learning: a case studyCode1
Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective—0
Control-Informed Reinforcement Learning for Chemical ProcessesCode1
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Rethinking State Disentanglement in Causal Reinforcement Learning—0
SUMO: Search-Based Uncertainty Estimation for Model-Based Offline Reinforcement Learning—0
Localized Observation Abstraction Using Piecewise Linear Spatial Decay for Reinforcement Learning in Combat Simulations—0
Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical Reinforcement Learning—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Domain Adaptation for Offline Reinforcement Learning with Limited Samples—0
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement LearningCode0
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Benchmark Results

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