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 1351–1375 of 15113 papers

TitleStatusHype
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement LearningCode1
Exploration by Random Network DistillationCode1
Exploration via Elliptical Episodic BonusesCode1
Exploration via Planning for Information about the Optimal TrajectoryCode1
Combining Modular Skills in Multitask LearningCode1
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal controlCode1
Fast Context Adaptation via Meta-LearningCode1
Fast Population-Based Reinforcement Learning on a Single MachineCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Feasibility Consistent Representation Learning for Safe Reinforcement LearningCode1
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with DroneCode1
Federated Ensemble-Directed Offline Reinforcement LearningCode1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
A Reinforcement Learning-based Volt-VAR Control Dataset and Testing EnvironmentCode1
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection ScenariosCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
COG: Connecting New Skills to Past Experience with Offline Reinforcement LearningCode1
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Benchmark Results

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