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 3221–3230 of 15113 papers

TitleStatusHype
Transferable Curricula through Difficulty Conditioned Generators—0
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
State-wise Constrained Policy OptimizationCode1
AdCraft: An Advanced Reinforcement Learning Benchmark Environment for Search Engine Marketing OptimizationCode0
Learning to Generate Better Than Your LLMCode1
Efficient Dynamics Modeling in Interactive Environments with Koopman Theory—0
Int-HRL: Towards Intention-based Hierarchical Reinforcement Learning—0
Reward Shaping via Diffusion Process in Reinforcement Learning—0
Neural Inventory Control in Networks via Hindsight Differentiable Policy OptimizationCode1
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap—0
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Benchmark Results

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