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 2226–2250 of 15113 papers

TitleStatusHype
CoRL: Environment Creation and Management Focused on System IntegrationCode1
Converting Biomechanical Models from OpenSim to MuJoCoCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Reinforcement Learning in High-frequency Market MakingCode1
Bridging State and History Representations: Understanding Self-Predictive RLCode1
A Crash Course on Reinforcement LearningCode1
COOL-MC: A Comprehensive Tool for Reinforcement Learning and Model CheckingCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Learning, Fast and Slow: A Goal-Directed Memory-Based Approach for Dynamic EnvironmentsCode1
Cooperative Multi-Agent Reinforcement Learning with Sequential Credit AssignmentCode1
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Mind the Gap: Offline Policy Optimization for Imperfect RewardsCode1
Learning Invariant Representations for Reinforcement Learning without ReconstructionCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector QuantizationCode1
Learning Selective Communication for Multi-Agent Path FindingCode1
Learning to swim in potential flowCode1
Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement LearningCode1
Managing power grids through topology actions: A comparative study between advanced rule-based and reinforcement learning agentsCode1
MoCoDA: Model-based Counterfactual Data AugmentationCode1
MOLUCINATE: A Generative Model for Molecules in 3D SpaceCode1
Counterfactual Data Augmentation using Locally Factored DynamicsCode1
Model-based Adversarial Meta-Reinforcement LearningCode1
On Pathologies in KL-Regularized Reinforcement Learning from Expert DemonstrationsCode1
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Benchmark Results

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