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 1451–1475 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven ExplorationCode1
LEDRO: LLM-Enhanced Design Space Reduction and Optimization for Analog CircuitsCode1
Faster Deep Reinforcement Learning with Slower Online NetworkCode1
Accelerating Reinforcement Learning with Learned Skill PriorsCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement LearningCode1
Deep Reinforcement Learning based Group Recommender SystemCode1
Deep-Reinforcement-Learning-based Path Planning for Industrial Robots using Distance Sensors as ObservationCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Actor-Critic Reinforcement Learning for Control with Stability GuaranteeCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learningCode1
Comparing Deep Reinforcement Learning Algorithms in Two-Echelon Supply ChainsCode1
A Game-Theoretic Approach to Multi-Agent Trust Region OptimizationCode1
A simple but strong baseline for online continual learning: Repeated Augmented RehearsalCode1
Deep Reinforcement Learning for Conservation DecisionsCode1
Deep Reinforcement Learning for Computational Fluid Dynamics on HPC SystemsCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Deep Reinforcement Learning for Resource Allocation in Business ProcessesCode1
Deep Reinforcement Learning in Parameterized Action SpaceCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
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Benchmark Results

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