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 1851–1875 of 15113 papers

TitleStatusHype
A Max-Min Entropy Framework for Reinforcement LearningCode1
Reinforcement Learning Gradients as Vitamin for Online Finetuning Decision TransformersCode1
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksCode1
Testing Stationarity and Change Point Detection in Reinforcement LearningCode1
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Reinforcement Learning Policy as Macro Regulator Rather than Macro PlacerCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Reinforcement Learning Under Moral UncertaintyCode1
Reinforcement Learning with Augmented DataCode1
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle RoutingCode1
Reinforcement Learning with Convex ConstraintsCode1
Reinforcement Learning with Dynamic Convex Risk MeasuresCode1
Reinforcement Learning with Model Predictive Control for Highway Ramp MeteringCode1
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement LearningCode1
AutoPhase: Juggling HLS Phase Orderings in Random Forests with Deep Reinforcement LearningCode1
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Reinforcement Learning with Random DelaysCode1
Reinforcement Learning with Sparse Rewards using Guidance from Offline DemonstrationCode1
Combining Modular Skills in Multitask LearningCode1
Reinforcement Learning with Videos: Combining Offline Observations with InteractionCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image EnhancementCode1
Replay-Guided Adversarial Environment DesignCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
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Benchmark Results

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