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 15511575 of 15113 papers

TitleStatusHype
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement LearningCode1
Human-Inspired Multi-Agent Navigation using Knowledge DistillationCode1
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving IntelligenceCode1
Lyapunov Barrier Policy OptimizationCode1
Inclined Quadrotor Landing using Deep Reinforcement LearningCode1
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics ModelCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution SystemsCode1
Solving Compositional Reinforcement Learning Problems via Task ReductionCode1
Large Batch Simulation for Deep Reinforcement LearningCode1
Generalizable Episodic Memory for Deep Reinforcement LearningCode1
XDO: A Double Oracle Algorithm for Extensive-Form GamesCode1
Iterative Shrinking for Referring Expression Grounding Using Deep Reinforcement LearningCode1
The AI Arena: A Framework for Distributed Multi-Agent Reinforcement LearningCode1
Reinforcement Learning with Prototypical RepresentationsCode1
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous RacingCode1
Comparing Popular Simulation Environments in the Scope of Robotics and Reinforcement LearningCode1
Behavior From the Void: Unsupervised Active Pre-TrainingCode1
A Crash Course on Reinforcement LearningCode1
MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world modelsCode1
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
Lyapunov-Regularized Reinforcement Learning for Power System Transient StabilityCode1
Learning Collision-free and Torque-limited Robot Trajectories based on Alternative Safe BehaviorsCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
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Benchmark Results

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