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 1301–1350 of 15113 papers

TitleStatusHype
Learning to Simulate Self-Driven Particles System with Coordinated Policy OptimizationCode1
Fault-Tolerant Federated Reinforcement Learning with Theoretical GuaranteeCode1
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement LearningCode1
Goal-Aware Cross-Entropy for Multi-Target Reinforcement LearningCode1
Recurrent Off-policy Baselines for Memory-based Continuous ControlCode1
Uniformly Conservative Exploration in Reinforcement LearningCode1
Understanding the World Through ActionCode1
False Correlation Reduction for Offline Reinforcement LearningCode1
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
An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agentsCode1
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender SystemCode1
No RL, No Simulation: Learning to Navigate without NavigatingCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
Accelerating lifelong reinforcement learning via reshaping rewardsCode1
Safe Driving via Expert Guided Policy OptimizationCode1
Offline Reinforcement Learning with Implicit Q-LearningCode1
StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement LearningCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
Safe Reinforcement Learning Using Robust Control Barrier FunctionsCode1
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise DatasetsCode1
MARVEL: Raster Manga Vectorization via Primitive-wise Deep Reinforcement LearningCode1
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
The Information Geometry of Unsupervised Reinforcement LearningCode1
Mismatched No More: Joint Model-Policy Optimization for Model-Based RLCode1
Replay-Guided Adversarial Environment DesignCode1
Multi-Agent Constrained Policy OptimisationCode1
CARL: A Benchmark for Contextual and Adaptive Reinforcement LearningCode1
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-EnsembleCode1
Large Batch Experience ReplayCode1
Offline Reinforcement Learning with Reverse Model-based ImaginationCode1
Scalable Online Planning via Reinforcement Learning Fine-TuningCode1
MOLUCINATE: A Generative Model for Molecules in 3D SpaceCode1
Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement LearningCode1
Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay RandomizationCode1
Know Your Action Set: Learning Action Relations for Reinforcement LearningCode1
Offline Reinforcement Learning with In-sample Q-LearningCode1
HyperDQN: A Randomized Exploration Method for Deep Reinforcement LearningCode1
Learning of Parameters in Behavior Trees for Movement SkillsCode1
Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous DrivingCode1
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning AlgorithmsCode1
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Benchmark Results

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