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 1141–1150 of 15113 papers

TitleStatusHype
From General to Specific: Tailoring Large Language Models for Personalized Healthcare—0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
Offline Reinforcement Learning for LLM Multi-Step ReasoningCode2
AdaCred: Adaptive Causal Decision Transformers with Feature Crediting—0
Single-Loop Federated Actor-Critic across Heterogeneous Environments—0
Offline Safe Reinforcement Learning Using Trajectory ClassificationCode0
Deep reinforcement learning with time-scale invariant memoryCode0
Simulation-Free Hierarchical Latent Policy Planning for Proactive Dialogues—0
Learning to Generate Research Idea with Dynamic Control—0
Bayesian Critique-Tune-Based Reinforcement Learning with Adaptive Pressure for Multi-Intersection Traffic Signal Control—0
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Benchmark Results

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