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

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Showing 721–730 of 15113 papers

TitleStatusHype
SRL: Scaling Distributed Reinforcement Learning to Over Ten Thousand CoresCode1
MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse RewardsCode1
Automatic Truss Design with Reinforcement LearningCode1
Learning to Modulate pre-trained Models in RLCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement LearningCode1
State-wise Constrained Policy OptimizationCode1
Learning to Generate Better Than Your LLMCode1
Neural Inventory Control in Networks via Hindsight Differentiable Policy OptimizationCode1
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Benchmark Results

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