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

Showing 12711280 of 15113 papers

TitleStatusHype
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Graph Convolutional Value Decomposition in Multi-Agent Reinforcement LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement LearningCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
A Practical Two-Stage Recipe for Mathematical LLMs: Maximizing Accuracy with SFT and Efficiency with Reinforcement LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagationCode1
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Benchmark Results

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