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 7801–7825 of 15113 papers

TitleStatusHype
On the (In)Tractability of Reinforcement Learning for LTL Objectives—0
SatNet: A Benchmark for Satellite Scheduling Optimization—0
Reversible Action Design for Combinatorial Optimization with ReinforcementLearning—0
Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation—0
Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy—0
Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks—0
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning—0
Generating GPU Compiler Heuristics using Reinforcement Learning—0
Inducing Functions through Reinforcement Learning without Task Specification—0
Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS—0
Independent Learning in Stochastic Games—0
An application of reinforcement learning to residential energy storage under real-time pricing—0
A Free Lunch from the Noise: Provable and Practical Exploration for Representation Learning—0
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation—0
Real-World Dexterous Object Manipulation based Deep Reinforcement LearningCode0
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures—0
Reinforcement Learning for Few-Shot Text Generation AdaptationCode0
UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning—0
Off-Policy Correction For Multi-Agent Reinforcement LearningCode0
Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning—0
Vulcan: Solving the Steiner Tree Problem with Graph Neural Networks and Deep Reinforcement Learning—0
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation—0
Reinforcement Learning with General LTL Objectives is Intractable—0
A Hybrid Neuro-Symbolic approach for Text-Based Games using Inductive Logic Programming—0
Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs—0
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Benchmark Results

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