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 3171–3180 of 15113 papers

TitleStatusHype
Offline Safe Reinforcement Learning Using Trajectory ClassificationCode0
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
Learning to Generate Research Idea with Dynamic Control—0
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues—0
Single-Loop Federated Actor-Critic across Heterogeneous Environments—0
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
Harvesting energy from turbulent winds with Reinforcement Learning—0
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models—0
Multi-Task Reinforcement Learning for Quadrotors—0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
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Benchmark Results

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