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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1726–1750 of 15113 papers

TitleStatusHype
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
Hierarchical Learning-based Graph Partition for Large-scale Vehicle Routing ProblemsCode1
Environmental effects on emergent strategy in micro-scale multi-agent reinforcement learningCode1
Evaluating the Performance of Reinforcement Learning AlgorithmsCode1
Harnessing Equivariance: Modeling Turbulence with Graph Neural NetworksCode1
BEAR: Physics-Principled Building Environment for Control and Reinforcement LearningCode1
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological ModelsCode1
Pretraining Representations for Data-Efficient Reinforcement LearningCode1
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
ERL-Re^2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy RepresentationCode1
Program Synthesis Guided Reinforcement Learning for Partially Observed EnvironmentsCode1
ESRL: Efficient Sampling-based Reinforcement Learning for Sequence GenerationCode1
Evaluating Soccer Player: from Live Camera to Deep Reinforcement LearningCode1
BayesSimIG: Scalable Parameter Inference for Adaptive Domain Randomization with IsaacGymCode1
Evaluating Long-Term Memory in 3D MazesCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Automated Cloud Provisioning on AWS using Deep Reinforcement LearningCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic CandidatesCode1
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample ComplexityCode1
Evolutionary Planning in Latent SpaceCode1
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
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Benchmark Results

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