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 2076–2100 of 15113 papers

TitleStatusHype
Reinforcement Learning via Fenchel-Rockafellar DualityCode1
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on HardwareCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
A Boolean Task Algebra for Reinforcement LearningCode1
Represented Value Function Approach for Large Scale Multi Agent Reinforcement LearningCode1
MushroomRL: Simplifying Reinforcement Learning ResearchCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
CURL: Contrastive Unsupervised Representation Learning for Reinforcement LearningCode1
An Optimistic Perspective on Offline Deep Reinforcement LearningCode1
Variational Imitation Learning with Diverse-quality DemonstrationsCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement LearningCode1
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot ControlCode1
PAC Confidence Sets for Deep Neural Networks via Calibrated PredictionCode1
Pseudo Random Number Generation: a Reinforcement Learning approachCode1
Imitation Learning via Off-Policy Distribution MatchingCode1
VALAN: Vision and Language Agent NavigationCode1
Simplified Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Dream to Control: Learning Behaviors by Latent ImaginationCode1
LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement LearningCode1
Staying up to Date with Online Content Changes Using Reinforcement Learning for SchedulingCode1
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization ProblemsCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
A Deep Reinforcement Learning Approach to First-Order Logic Theorem ProvingCode1
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement LearningCode1
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Benchmark Results

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