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 2226–2250 of 15113 papers

TitleStatusHype
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning—0
Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term RetentionCode0
Distributionally Robust Reinforcement Learning with Interactive Data Collection: Fundamental Hardness and Near-Optimal Algorithm—0
REACT: Revealing Evolutionary Action Consequence Trajectories for Interpretable Reinforcement Learning—0
SliceIt! -- A Dual Simulator Framework for Learning Robot Food SlicingCode0
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station ReliefCode1
Reinforcement Learning in Categorical Cybernetics—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Asymptotics of Language Model Alignment—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Entity-Centric Reinforcement Learning for Object Manipulation from PixelsCode1
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control—0
Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning—0
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods—0
Molecular Generative Adversarial Network with Multi-Property Optimization—0
Learning Visual Quadrupedal Loco-Manipulation from Demonstrations—0
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning—0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment—0
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
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Benchmark Results

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