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 1301–1325 of 15113 papers

TitleStatusHype
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement LearningCode1
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Recurrent Off-policy Baselines for Memory-based Continuous ControlCode1
Uniformly Conservative Exploration in Reinforcement LearningCode1
Goal-Aware Cross-Entropy for Multi-Target Reinforcement LearningCode1
False Correlation Reduction for Offline Reinforcement LearningCode1
Understanding the World Through ActionCode1
A Versatile and Efficient Reinforcement Learning Framework for Autonomous DrivingCode1
Sequential Voting with Relational Box Fields for Active Object DetectionCode1
LOA: Logical Optimal Actions for Text-based Interaction GamesCode1
Hierarchical Skills for Efficient ExplorationCode1
CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning AgentsCode1
Offline Reinforcement Learning with Value-based Episodic MemoryCode1
Contrastive Active InferenceCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agentsCode1
No RL, No Simulation: Learning to Navigate without NavigatingCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Safe Driving via Expert Guided Policy OptimizationCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement LearningCode1
Offline Reinforcement Learning with Implicit Q-LearningCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
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Benchmark Results

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