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 2441–2450 of 15113 papers

TitleStatusHype
Real-World Fluid Directed Rigid Body Control via Deep Reinforcement Learning—0
Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices—0
Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy OptimizationCode0
Differentially Private Deep Model-Based Reinforcement Learning—0
Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement LearningCode2
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RLCode0
QGFN: Controllable Greediness with Action ValuesCode1
Convergence for Natural Policy Gradient on Infinite-State Queueing MDPs—0
Safety Filters for Black-Box Dynamical Systems by Learning Discriminating HyperplanesCode1
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
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Benchmark Results

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