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 751–775 of 15113 papers

TitleStatusHype
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
Deep reinforcement learning-designed radiofrequency waveform in MRICode1
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
Deep RL Agent for a Real-Time Action Strategy GameCode1
DROPO: Sim-to-Real Transfer with Offline Domain RandomizationCode1
Bidirectional Model-based Policy OptimizationCode1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Asynchronous Methods for Deep Reinforcement LearningCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
Deep Reinforcement Learning with Gradient Eligibility TracesCode1
A Sustainable Ecosystem through Emergent Cooperation in Multi-Agent Reinforcement LearningCode1
A SWAT-based Reinforcement Learning Framework for Crop ManagementCode1
B-Pref: Benchmarking Preference-Based Reinforcement LearningCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
EDGE: Explaining Deep Reinforcement Learning PoliciesCode1
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous ControlCode1
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Benchmark Results

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