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 4301–4325 of 15113 papers

TitleStatusHype
Safe Policy Optimization with Local Generalized Linear Function ApproximationsCode0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
Reinforcement Learning Agents in Colonel BlottoCode0
Two-step dynamic obstacle avoidanceCode0
Two steps to risk sensitivityCode0
Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCSCode0
PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPUCode0
Reinforcement Learning and Adaptive Sampling for Optimized DNN CompilationCode0
PixelRL: Fully Convolutional Network with Reinforcement Learning for Image ProcessingCode0
Reinforcement Learning and Deep Learning based Lateral Control for Autonomous DrivingCode0
Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage PlanningCode0
Online Game Level Generation from MusicCode0
Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approachCode0
Safe Reinforcement Learning From Pixels Using a Stochastic Latent RepresentationCode0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
UNAS: Differentiable Architecture Search Meets Reinforcement LearningCode0
Safe Reinforcement Learning in Black-Box Environments via Adaptive ShieldingCode0
Safe Reinforcement Learning of Control-Affine Systems with Vertex NetworksCode0
Safe Reinforcement Learning Using Black-Box Reachability AnalysisCode0
MOSEAC: Streamlined Variable Time Step Reinforcement LearningCode0
Uncertainty-Aware Reward-Free Exploration with General Function ApproximationCode0
Reinforcement Learning Approaches for Traffic Signal Control under Missing DataCode0
Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data CorruptionsCode0
Safe Reinforcement Learning with Nonlinear Dynamics via Model Predictive ShieldingCode0
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Benchmark Results

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