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 14711480 of 15113 papers

TitleStatusHype
First-Explore, then Exploit: Meta-Learning to Solve Hard Exploration-Exploitation Trade-OffsCode1
Flexible Attention-Based Multi-Policy Fusion for Efficient Deep Reinforcement LearningCode1
LOA: Logical Optimal Actions for Text-based Interaction GamesCode1
Local policy search with Bayesian optimizationCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular NetworksCode1
Deep Reinforcement Learning for List-wise RecommendationsCode1
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement LearningCode1
Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
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Benchmark Results

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