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 19762000 of 15113 papers

TitleStatusHype
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage RegressionCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement LearningCode1
Integrating Deep Reinforcement Learning with Model-based Path Planners for Automated DrivingCode1
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement LearningCode1
When should we prefer Decision Transformers for Offline Reinforcement Learning?Code1
Human-Inspired Multi-Agent Navigation using Knowledge DistillationCode1
Human-Level Control through Directly-Trained Deep Spiking Q-NetworksCode1
Battlesnake Challenge: A Multi-agent Reinforcement Learning Playground with Human-in-the-loopCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Information Design in Multi-Agent Reinforcement LearningCode1
SPEED-RL: Faster Training of Reasoning Models via Online Curriculum LearningCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
SQIL: Imitation Learning via Reinforcement Learning with Sparse RewardsCode1
SQUIRE: A Sequence-to-sequence Framework for Multi-hop Knowledge Graph ReasoningCode1
SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement LearningCode1
Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand SystemsCode1
Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage ControlCode1
Bayesian Generational Population-Based TrainingCode1
HYDRA: A Hyper Agent for Dynamic Compositional Visual ReasoningCode1
An Alternative Softmax Operator for Reinforcement LearningCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
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Benchmark Results

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