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

Showing 12261250 of 15113 papers

TitleStatusHype
Sample Efficient Deep Reinforcement Learning via Uncertainty EstimationCode1
Using Simulation Optimization to Improve Zero-shot Policy Transfer of QuadrotorsCode1
Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanismCode1
SimSR: Simple Distance-based State Representation for Deep Reinforcement LearningCode1
Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control OptimizationCode1
Reinforcement Learning with Dynamic Convex Risk MeasuresCode1
Lane Change Decision-Making through Deep Reinforcement LearningCode1
Learning to Walk with Dual Agents for Knowledge Graph ReasoningCode1
Safety and Liveness Guarantees through Reach-Avoid Reinforcement LearningCode1
Direct Behavior Specification via Constrained Reinforcement LearningCode1
Maximum Entropy Population-Based Training for Zero-Shot Human-AI CoordinationCode1
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Variational Quantum Soft Actor-CriticCode1
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlCode1
Space Non-cooperative Object Active Tracking with Deep Reinforcement LearningCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Learning to Share in Multi-Agent Reinforcement LearningCode1
Stochastic Actor-Executor-Critic for Image-to-Image TranslationCode1
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image RegistrationCode1
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Human-Level Control through Directly-Trained Deep Spiking Q-NetworksCode1
PantheonRL: A MARL Library for Dynamic Training InteractionsCode1
Tree-based Focused Web Crawling with Reinforcement LearningCode1
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical LocomotionCode1
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Benchmark Results

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