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 1976–2000 of 15113 papers

TitleStatusHype
Self-Paced Deep Reinforcement LearningCode1
Model-Based Meta-Reinforcement Learning for Flight with Suspended PayloadsCode1
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty EstimationCode1
Chip Placement with Deep Reinforcement LearningCode1
Energy-Based Imitation LearningCode1
Continual Reinforcement Learning with Multi-Timescale ReplayCode1
Fast Template Matching and Update for Video Object Tracking and SegmentationCode1
MARLeME: A Multi-Agent Reinforcement Learning Model Extraction LibraryCode1
Zero-Shot Compositional Policy Learning via Language GroundingCode1
Prolog Technology Reinforcement Learning ProverCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
PatchAttack: A Black-box Texture-based Attack with Reinforcement LearningCode1
Topological Quantum Compiling with Reinforcement LearningCode1
Adaptive Transformers in RLCode1
Continual Learning with Gated Incremental Memories for sequential data processingCode1
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward DecompositionCode1
CURL: Contrastive Unsupervised Representations for Reinforcement LearningCode1
An Application of Deep Reinforcement Learning to Algorithmic TradingCode1
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement LearningCode1
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement LearningCode1
Action Space Shaping in Deep Reinforcement LearningCode1
Multi-Task Reinforcement Learning with Soft ModularizationCode1
Agent57: Outperforming the Atari Human BenchmarkCode1
Deep reinforcement learning for large-scale epidemic controlCode1
Ultrasound-Guided Robotic Navigation with Deep Reinforcement LearningCode1
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Benchmark Results

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