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 3701–3725 of 15113 papers

TitleStatusHype
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach—0
Limited Query Graph Connectivity Test—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
Exponential Hardness of Reinforcement Learning with Linear Function Approximation—0
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process—0
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning—0
The Dormant Neuron Phenomenon in Deep Reinforcement LearningCode6
Logarithmic Switching Cost in Reinforcement Learning beyond Linear MDPs—0
GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual ExplanationsCode1
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
Neural Laplace Control for Continuous-time Delayed SystemsCode1
Model-Based Uncertainty in Value FunctionsCode1
EvoTorch: Scalable Evolutionary Computation in PythonCode3
Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization—0
Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains—0
AC2C: Adaptively Controlled Two-Hop Communication for Multi-Agent Reinforcement Learning—0
VIPeR: Provably Efficient Algorithm for Offline RL with Neural Function ApproximationCode0
Energy Harvesting Reconfigurable Intelligent Surface for UAV Based on Robust Deep Reinforcement LearningCode1
Diverse Policy Optimization for Structured Action SpaceCode1
To the Noise and Back: Diffusion for Shared Autonomy—0
Concept Learning for Interpretable Multi-Agent Reinforcement Learning—0
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
Behavior Proximal Policy OptimizationCode1
Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks—0
Self-supervised network distillation: an effective approach to exploration in sparse reward environmentsCode0
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Benchmark Results

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