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

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Showing 1651–1700 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
Unsupervised-to-Online Reinforcement Learning—0
Simultaneous Training of First- and Second-Order Optimizers in Population-Based Reinforcement Learning—0
Learning Robust Reward Machines from Noisy LabelsCode0
MiWaves Reinforcement Learning AlgorithmCode0
What makes math problems hard for reinforcement learning: a case studyCode1
Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper—0
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
Rethinking State Disentanglement in Causal Reinforcement Learning—0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
Reinforcement Learning for Causal Discovery without Acyclicity Constraints—0
Mastering the Digital Art of War: Developing Intelligent Combat Simulation Agents for Wargaming Using Hierarchical 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
Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards—0
Query-Efficient Video Adversarial Attack with Stylized Logo—0
Leveraging Unlabeled Data Sharing through Kernel Function Approximation in Offline Reinforcement LearningCode0
Domain Adaptation for Offline Reinforcement Learning with Limited Samples—0
Advances in Preference-based Reinforcement Learning: A Review—0
Using Part-based Representations for Explainable Deep Reinforcement Learning—0
Integrating Multi-Modal Input Token Mixer Into Mamba-Based Decision Models: Decision MetaMamba—0
Offline Model-Based Reinforcement Learning with Anti-Exploration—0
Hokoff: Real Game Dataset from Honor of Kings and its Offline Reinforcement Learning BenchmarksCode2
Accelerating Goal-Conditioned RL Algorithms and ResearchCode3
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm—0
MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions—0
World Models Increase Autonomy in Reinforcement Learning—0
Multi-Agent Reinforcement Learning for Autonomous Driving: A SurveyCode5
Enhancing Reinforcement Learning Through Guided Search—0
Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning—0
Reinforcement learning-based adaptive speed controllers in mixed autonomy condition—0
Vanilla Gradient Descent for Oblique Decision TreesCode0
Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds—0
Fine-tuning LLMs for Autonomous Spacecraft Control: A Case Study Using Kerbal Space ProgramCode1
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
Neural Reward MachinesCode0
Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct OptimizationCode1
Online Behavior Modification for Expressive User Control of RL-Trained Robots—0
BCR-DRL: Behavior- and Context-aware Reward for Deep Reinforcement Learning in Human-AI Coordination—0
Experimental evaluation of offline reinforcement learning for HVAC control in buildingsCode0
Adaptive User Journeys in Pharma E-Commerce with Reinforcement Learning: Insights from SwipeRx—0
Off-Policy Reinforcement Learning with High Dimensional Reward—0
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Benchmark Results

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