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 1151–1160 of 15113 papers

TitleStatusHype
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
SUBER: An RL Environment with Simulated Human Behavior for Recommender SystemsCode1
Emergent behavior and neural dynamics in artificial agents tracking turbulent plumesCode1
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning ApproachCode1
Avalanche RL: a Continual Reinforcement Learning LibraryCode1
Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulasCode1
End-to-End Affordance Learning for Robotic ManipulationCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
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Benchmark Results

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