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 15511560 of 15113 papers

TitleStatusHype
Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement LearningCode1
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving IntelligenceCode1
Human-Inspired Multi-Agent Navigation using Knowledge DistillationCode1
Inclined Quadrotor Landing using Deep Reinforcement LearningCode1
Lyapunov Barrier Policy OptimizationCode1
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
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Benchmark Results

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