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 1551–1575 of 15113 papers

TitleStatusHype
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Generalization to New Actions in Reinforcement LearningCode1
Agents that Listen: High-Throughput Reinforcement Learning with Multiple Sensory SystemsCode1
Differentiable Trust Region Layers for Deep Reinforcement LearningCode1
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
A Deep Reinforced Model for Abstractive SummarizationCode1
Diffusion Policies creating a Trust Region for Offline Reinforcement LearningCode1
Generalizable Visual Reinforcement Learning with Segment Anything ModelCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement LearningCode1
Direct Behavior Specification via Constrained Reinforcement LearningCode1
A2C is a special case of PPOCode1
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Discovering General Reinforcement Learning Algorithms with Adversarial Environment DesignCode1
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive LearningCode1
Batch Exploration with Examples for Scalable Robotic Reinforcement LearningCode1
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Benchmark Results

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