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 911920 of 15113 papers

TitleStatusHype
Conservative and Adaptive Penalty for Model-Based Safe Reinforcement LearningCode1
Generalizable Episodic Memory for Deep Reinforcement LearningCode1
Digital Twin-Enhanced Wireless Indoor Navigation: Achieving Efficient Environment Sensing with Zero-Shot Reinforcement LearningCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
ConfuciuX: Autonomous Hardware Resource Assignment for DNN Accelerators using Reinforcement LearningCode1
Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint GeneratorsCode1
Conservative Offline Distributional Reinforcement LearningCode1
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal ReasoningCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Continual Backprop: Stochastic Gradient Descent with Persistent RandomnessCode1
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Benchmark Results

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