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 461–470 of 15113 papers

TitleStatusHype
Distilling Reinforcement Learning Algorithms for In-Context Model-Based PlanningCode1
VEM: Environment-Free Exploration for Training GUI Agent with Value Environment ModelCode1
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope?Code1
Learning to Sample Effective and Diverse Prompts for Text-to-Image GenerationCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
DuoGuard: A Two-Player RL-Driven Framework for Multilingual LLM GuardrailsCode1
Analytical Lyapunov Function Discovery: An RL-based Generative ApproachCode1
GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic EnvironmentsCode1
SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI Interaction ExperimentsCode1
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Benchmark Results

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