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 791–800 of 15113 papers

TitleStatusHype
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constrained Update Projection Approach to Safe Policy OptimizationCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
Distributional Reinforcement Learning via Moment MatchingCode1
Consistent Paths Lead to Truth: Self-Rewarding Reinforcement Learning for LLM ReasoningCode1
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
DMC-VB: A Benchmark for Representation Learning for Control with Visual DistractorsCode1
DMR: Decomposed Multi-Modality Representations for Frames and Events Fusion in Visual Reinforcement LearningCode1
Active Inference for Stochastic ControlCode1
Constrained Variational Policy Optimization for Safe Reinforcement LearningCode1
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Benchmark Results

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