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

Showing 38513875 of 15113 papers

TitleStatusHype
Challenging common bolus advisor for self-monitoring type-I diabetes patients using Reinforcement LearningCode0
Deep reinforcement learning with time-scale invariant memoryCode0
Fourier Features in Reinforcement Learning with Neural NetworksCode0
Challenges of Context and Time in Reinforcement Learning: Introducing Space Fortress as a BenchmarkCode0
Challenges in High-dimensional Reinforcement Learning with Evolution StrategiesCode0
FORLORN: A Framework for Comparing Offline Methods and Reinforcement Learning for Optimization of RAN ParametersCode0
Foresee then Evaluate: Decomposing Value Estimation with Latent Future PredictionCode0
FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough ReproductionCode0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
Generative Planning for Temporally Coordinated Exploration in Reinforcement LearningCode0
Flexible Option LearningCode0
ROBEL: Robotics Benchmarks for Learning with Low-Cost RobotsCode0
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement LearningCode0
Flight Controller Synthesis Via Deep Reinforcement LearningCode0
Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and AnimalsCode0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
CGAR: Critic Guided Action Redistribution in Reinforcement LeaningCode0
CFlowNets: Continuous Control with Generative Flow NetworksCode0
Answers Unite! Unsupervised Metrics for Reinforced Summarization ModelsCode0
Baconian: A Unified Open-source Framework for Model-Based Reinforcement LearningCode0
Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement LearningCode0
Deep Spatial Autoencoders for Visuomotor LearningCode0
Deep Successor Reinforcement LearningCode0
Certification of Iterative Predictions in Bayesian Neural NetworksCode0
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation ProblemCode0
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Benchmark Results

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